Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System by Tingting Wu, Yufen Zhuang, Bi Fan, Hainan Guo, Wei Fan, Cai Yi, Kangkang Xu

“…Time-domain and frequency-domain features are extracted from the different speeds vibration signals. …”
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SFEF-Net: Scattering Feature Extraction and Fusion Network for Aircraft Detection in SAR Images by Qiang Zhou, Zongxu Pan, Ben Niu

“…However, this task presents significant challenges, including extracting discrete scattering features, mitigating interference from complex backgrounds, and handling potential label noise. …”
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Improved RT-DETR for Infrared Ship Detection Based on Multi-Attention and Feature Fusion by Chun Liu, Yuanliang Zhang, Jingfu Shen, Feiyue Liu

“…Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. …”
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Developing a hybrid feature selection method to detect botnet attacks in IoT devices by Alshaeaa H.Y., Ghadhban Z.M., Ministry of Education, Iraq

“…This article aims to develop a hybrid feature selection method to find the most influential features based on three feature selection methods, correlation, generalized normal distribution optimization, and lasso, to detect botnet attacks in IoT devices. …”
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MSDNet: A Multi-Scale Feature Representation Network Model for Tunnel Bolt Detection by Xiufeng Wu, Xueli Li, Guangxing Cao, Chao Guo, Wei Chang, Zhao Wang, Shuliang Liu

“…However, identifying a large number of corroded bolts presents challenges, particularly due to interference from complex background noise and the inherent limitations of CNNs in extracting local features. …”
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HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images by Pengfei Zhang, Jian Liu, Jianqiang Zhang, Yiping Liu, Jiahao Shi

“…DyCoMF-Arch builds a hierarchical feature pyramid using multistage spatial compression and expansion, with dynamic weight allocation to extract salient features. …”
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Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways by Bo Gu, Jianfeng Feng, Zhuoyi Song

“…Existing insect‐inspired looming detection models typically rely on either motion‐pathway or feature‐pathway signals, yet both are susceptible to dynamic visual scene interference. …”
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Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM by Zhanrong Li, Chao Jiang, Yu Sun, Haosheng Su, Longning He

“…Subsequently, the expanded mask is intersected with instance-level semantic segmentation results to precisely delineate dynamic areas, effectively constraining the search space for feature matching and reducing interference caused by dynamic objects. …”
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Vehicle detection method based on multi-layer selective feature for UAV aerial images by Yinbao Ma, Yuyu Meng, Jiuyuan Huo

“…In the neck, a multi-layer selective feature fusion pyramid (MS-FPN) is constructed to perform attention-based filtering on high-level semantic features and low-level spatial details, followed by feature enhancement via multiplication and global semantic refinement through residual connections. …”
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Dynamic Bidirectional Feature Enhancement Network for Thin Cloud Removal in Remote Sensing Images by Yu Wang, Hao Chen, Ye Zhang, Guozheng Li

“…These modules efficiently capture long-range contextual dependencies and suppress cloud noise interference. Next, an adaptive local feature enhancement block is constructed using cross-fusion and adjacent feature propagation between dynamic convolutions, aimed at enhancing the ability of model to recover details. …”
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An Accurate LiDAR-Inertial SLAM Based on Multi-Category Feature Extraction and Matching by Nuo Li, Yiqing Yao, Xiaosu Xu, Shuai Zhou, Taihong Yang

“…Despite its importance, existing optimization-based LiDAR-inertial SLAM methods often face key limitations: unreliable feature extraction, sensitivity to noise and sparsity, and the inclusion of redundant or low-quality feature correspondences. …”
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A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction by Jiaxin Huang, Huicong Wang, Yuhan Li, Shijian Liu

“…Additionally, a feature-driven module is cascaded at the end of the IDSR network to guide the high-resolution (HR) image reconstruction with feature prior information from a detection backbone. …”
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Cross-domain topic transfer learning method based on multiple balance and feature fusion by Zhenshun Xu, Zhenbiao Wang, Wenhao Zhang, Zengjin Tang

“…This research explores alternative methods for heterogeneous transfer learning in topic model beyond traditional parameter sharing, seeking to maximize the utilization of source domain knowledge and features, mitigating the interference of negative transfer. …”
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MSFE-Net: Multi-Scale Feature Enhancement Network for Remote Sensing Object Detection by Kai Yuan, Xing Li, Yaoyao Ren, Lianpeng Zhang, Wei Liu, Erzhu Li

“…To address this, we propose MSFE-Net, a multi-scale feature enhancement network designed to effectively suppress background interference and detect adjacent similar targets. …”
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Deep Learning-Based Feature Matching Algorithm for Multi-Beam and Side-Scan Images by Yu Fu, Xiaowen Luo, Xiaoming Qin, Hongyang Wan, Jiaxin Cui, Zepeng Huang

“…To address this issue, this paper proposes a feature matching network based on the LoFTR algorithm, utilizing the intermediate layers of the ResNet-50 network to extract shared features between the two types of images. …”
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Image data-driven intelligent recognition of permafrost strength and feature visualization based analysis by Zhaoming YAO, Xun WANG, Hang WEI, Xiaolong WANG

“…It was found that the model could extract and analyze key image features of frozen soil, enabling rapid strength assessment. …”
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Target Recognition Method Based on Graph Structure Perception of Invariant Features for SAR Images by Jingyi CAO, Yang ZHANG, Ya’nan YOU, Yamin WANG, Feng YANG, Weijia REN, Jun LIU

“…Specifically, to support essential feature extraction in each branch, we design a feature-guided graph feature perception module based on multilevel essential feature modeling. …”
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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…By integrating features across different scales, C2f-K effectively reduces background noise and interference, thereby improving the understanding of complex scenes of the model. …”
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Connecting communities: Enhancing broadband access in rural Tanzania through small cell deployment by Pascal Yamakili, Mrindoko Rashid Nicholaus

“…Methods: A framework is designed by leveraging LTE-Advanced features and integrating Macro cells and small cells in the WinProp tool for wireless propagation and radio network planning. …”
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Research of the total spectral density of signals in the LTE up-link by A. A. Novikova, V. M. Kozel, K. A. Kavaliou

“…The conclusion is made about the possibility of using an equivalent uniform spectral density to describe interference effects from groupings of subscriber terminals of LTE communication networks.…”
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