Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar by Jialin Liu, Xiaosong Tang, Feng Yang, Xu Qiao, Fanruo Li, Suping Peng, Xinxin Huang, Yuanjin Fang, Maoxuan Xu

“…The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. …”
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Modeling and Optimization of Energy Efficiency in 5G mmWave Networks With JT-CoMP Under Different Deployment Conditions by Mohamad Younes, Laurent Quibus

“…In the face of exponential growth in mobile traffic and network densification, reducing energy consumption is a strategic challenge for the sustainability of 5G networks, particularly in the millimeter wave (mmWave) bands, where high propagation losses and inter-cell interference make optimizing Energy Efficiency (EE) a complex task. …”
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PFARN: Pyramid Fusion Attention and Refinement Network for Multiscale Ship Detection in SAR Images by Ke Li, Hang Yu, Suqi Li, Shanhu Chen, Bailu Wang

“…First, a shape scale convolution is used to improve the focus and extraction of mutiscale-ship features. Second, the feature maps are fused with pyramid fusion attention, which is based on self-attention and Gaussian cross-attention, ensuring alignment of both the semantic and spatial information. …”
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JCN: Joint Constraint-Based Human Pose Refinement Networks by Yuru Zhang, Jiayuan Zhao, Xiaodong Su, Hongyan Xu, Meijian Jin

“…It selects an appropriate feature fusion strategy to fuse parallel branch features to improve model robustness. …”
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Computer-Vision-Based Product Quality Inspection and Novel Counting System by Changhyun Lee, Yunsik Kim, Hunkee Kim

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YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection by Jianhua Liu, Jing Guo, Suxin Zhang

“…This model incorporates a hybrid channel–space attention mechanism to enhance its attention to key features and to reduce interference from complex backgrounds. …”
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MAS-YOLOv11: An Improved Underwater Object Detection Algorithm Based on YOLOv11 by Yang Luo, Aiping Wu, Qingqing Fu

“…To address the challenges of underwater target detection, including complex background interference, light attenuation, severe occlusion, and overlap between targets, as well as the wide-scale variation in objects, we propose MAS-YOLOv11, an improved model integrating three key enhancements: First, we introduce the C2PSA_MSDA module, which integrates multi-scale dilated attention (MSDA) into the C2PSA module of the backbone, enhancing multi-scale feature representation via dilated convolutions and cross-scale attention. …”
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ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation by Yingqi Lu, Xiangsuo Fan, Jinfeng Wang, Shaojun Chen, Jie Meng

“…Specifically, the multi-scale parallel fusion mechanism introduced in ParaU-Net better captures the fine features of nodules and reduces interference from other structures. …”
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SODRS: Semisupervised Learning for One-Stage Small Object Detection in Remote Sensing Images by Mingquan Liu, Lei Kuang, Chengjun Li, Jing Tian, Zifang Chen, Xuewu Han

“…Small object detection in remote sensing images faces challenges such as weak features, vulnerability to interference, and limited object visibility. …”
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A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification by Ziwei Li, Weiming Xu, Shiyu Yang, Juan Wang, Hua Su, Zhanchao Huang, Sheng Wu

“…However, redundant background interference, varying feature scales, and high interclass similarity in remote sensing images present significant challenges for RSSC. …”
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Fine-grained image classification using the MogaNet network and a multi-level gating mechanism by Dahai Li, Su Chen

“…Meanwhile, a multi-level gating mechanism is introduced to obtain the saliency features of images. A feature elimination strategy is proposed to suppress the interference of fuzzy class features and background noise. …”
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A Rolling-Bearing-Fault Diagnosis Method Based on a Dual Multi-Scale Mechanism Applicable to Noisy-Variable Operating Conditions by Jing Kang, Taiyong Wang, Ye Wei, Usman Haladu Garba, Ying Tian

“…Subsequently, we introduce the Dynamic Weighted Multi-Scale Feature Convolutional Neural Network (DWMFCNN) model, which integrates two structures: multi-scale feature extraction and dynamic weighting of these features. …”
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Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments by Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN

“…MethodsThe MFCC is used to extract features from vibration signals contaminated by noise. …”
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RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8 by Yifan Wei, Jun Tao, Wenjun Wu, Donghua Yuan, Shunzhi Hou

“…Firstly, a combination of hybrid inflated convolution and RefConv is used to redesign the lightweight Ref-Dilated convolution block, which reduces the model computation. Second, a new feature pyramid network fusion module, the Hybrid Bridge Feature Pyramid Network (HBFPN), is designed to fuse the deep features with the high-level features, as well as the features of the current layer, to improve the feature extraction capability for fuzzy objects. …”
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CDBA-GAN: A Conditional Dual-Branch Attention Generative Adversarial Network for Robust Sonar Image Generation by Wanzeng Kong, Han Yang, Mingyang Jia, Zhe Chen

“…The framework comprises three key innovations: The conditional information fusion module, dual-branch attention feature fusion mechanism, and cross-layer feature reuse. …”
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A New Reliable Switched-Capacitor-Based High Step-Up Five-Level Inverter by Milad Ghavipanjeh Marangalu, Naser Vosoughi Kurdkandi, Kourosh Khalaj Monfared, Yousef Neyshabouri, Hani Vahedi

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IRSD-Net: An Adaptive Infrared Ship Detection Network for Small Targets in Complex Maritime Environments by Yitong Sun, Jie Lian

“…To further improve cross-scale fusion, we design the Dynamic Cross-Scale Feature Pyramid Network (DCSFPN), a bidirectional architecture that combines up- and downsampling to integrate low-level detail with high-level semantics. …”
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A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin by Haonan Jiang, Yanbo Zhao, Qiao Zhu, Yuanli Cai

“…Second, to improve the accuracy of sequence reconstruction under large noise levels, a PRN is established to obtain reference features, which are weighted and fused with the features of observed data. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…Additionally, it designs a detection decoupling head with a multi-head attention mechanism to improve the issue of network complexity caused by convolutional layer redundancy, processes spatial dimensions to focus on capturing target features, reduces interference from irrelevant backgrounds, and enhances the accuracy of occluded target recognition.urthermore, it constructs a lightweight neural network with dual convolution (CDC), enhancing inter-channel information flow, improving model feature expression capability, and reducing model complexity. …”
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Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition by Haodong Chi, Huiyuan Chen

“…To address the issues of non-stationarity, noise interference, and insufficient discriminative power of traditional fault feature extraction methods in rolling bearing vibration signals, this paper proposes a fault diagnosis method based on multi-scale permutation entropy (MPE) and a multi-strategy improved sparrow search algorithm (MSSA) under local mean decomposition (LMD). …”
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