Time synchronization attack detection for industrial wireless network by Sichao ZHANG, Wei LIANG, Xudong YUAN, Yinlong ZHANG, Meng ZHENG

“…High-precision time synchronization is the basis for ensuring the secure and reliable transmission of industrial wireless network (IWN).Delay attacks, as a class of time synchronization attacks which cannot be solved by cryptographic techniques, seriously threaten the secure operation of IWN.Firstly, based on the in-depth analysis on the time synchronization mechanisms of IWN, three-time synchronization attack models were proposed, including the one-way full life cycle delay attack, two-way full life cycle delay attack, and one-way non-full-life cycle delay attack.Stealthier delay attacks could be realized by the attack models under the premise that target nodes were not captured.Secondly, considering the problem that existing detection algorithms are difficult to detect stealthier delay attacks without obvious changes in time features, an attack detection algorithm based on a Bayesian model was proposed that extracts four representative features, including transmission rate, transmission delay, transmission success rate and time synchronization interval.In addition, in order to ensure the accuracy of the attack detection and classification in the presence of noise interference, the noise model of wireless channel was introduced to the Bayesian feature information matrix.Experimental results show that the proposed algorithm can effectively detect three kinds of attacks in the presence of noise.…”
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An advanced three stage lightweight model for underwater human detection by Zichen Liao, Kai Hu, Yuancheng Meng, Shuai Shen

“…The proposed model incorporates several innovative components: a comprehensive underwater dataset construction methodology, a StarBlock-based backbone structure for efficient feature extraction, a Context Anchor Attention (CAA) mechanism integrated into both backbone and neck components, and a Shared Convolution Batch Normalization (SCBN) detection head. …”
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An Enhanced Shuffle Attention with Context Decoupling Head with Wise IoU Loss for SAR Ship Detection by Yunshan Tang, Yue Zhang, Jiarong Xiao, Yue Cao, Zhongjun Yu

“…However, challenges remain with complex backgrounds and multi-scale ship targets amidst significant interference. This paper introduces a novel method that features a context-based decoupled head, leveraging positioning and semantic information, and incorporates shuffle attention to enhance feature map interpretation. …”
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Foreign object detection for mining conveyor belts based on YOLOv5n-CND by SUN Aoran, ZHAO Peipei, YANG Di, ZHANG Junyi, YU Hongjian

“…First, the C2f module was used to optimize the feature pyramid, using fewer parameters to address the issue of poor sensitivity to small object detection caused by the complex background in foreign object images and interference from complex objects in underground environments. …”
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Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network by Baoling Liu, Jun He, Xiaocui Yuan, Huiling Hu, Xuan Zeng, Zhifang Zhu, Jie Peng

“…However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. …”
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A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance by Ye Mu, Jinghuan Hu, Zhipeng Li, Heyang Wang, He Gong, Yu Sun, Tianli Hu

“…For multi-scale feature optimization, a Multi-Scale Feature Enhancement (MSFE) module is designed to dynamically prioritize task-relevant features. …”
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Wind load impact on tall building facades: damage observations during severe wind events and wind tunnel testing by Omar Metwally, Haitham A. Ibrahim, Amal Elawady, Amal Elawady, Ioannis Zisis, Ioannis Zisis, Arindam Gan Chowdhury, Arindam Gan Chowdhury

“…As global urbanization accelerates, the construction of tall buildings has surged, becoming a defining feature of modern cityscapes. Tall buildings, while contributing to economic growth and urban development, face substantial risks from extreme wind events, such as hurricanes and downbursts. …”
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3D-SCUMamba: An Abdominal Tumor Segmentation Model by Juwita, Ghulam Mubashar Hassan, Amitava Datta

“…The SC module effectively prepares summarized features for robust global dependency processing by the Mamba component. 3D-SCUMamba explicitly addresses the prevalent limitations in current deep learning methods by prioritizing robust feature representation, training stability, and substantial accuracy improvements. …”
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FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection by Zhijie Li, Jiahui Zhang, Yingjie Zhang, Dawei Yan, Xing Zhang, Marcin Woźniak, Wei Dong

“…The fuzzy Adapter Transformer Encoder utilizes adaptive fuzzy membership functions and rule-based smoothing to preserve critical details, such as edges and textures, while mitigating the loss of fine details in global feature processing. Meanwhile, the Fuzzy Denoising Transformer Decoder effectively reduces noise interference and enhances fine-grained feature capture, eliminating redundant computations in irrelevant regions. …”
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SMEA-YOLOv8n: A Sheep Facial Expression Recognition Method Based on an Improved YOLOv8n Model by Wenbo Yu, Xiang Yang, Yongqi Liu, Chuanzhong Xuan, Ruoya Xie, Chuanjiu Wang

“…Firstly, the proposed method integrates the parameter-free Similarity-Aware Attention Mechanism (SimAM) and MobileViTAttention modules into the CSP Bottleneck with 2 Convolutions(C2f) module of the neck network, aiming to enhance the model’s feature representation and fusion capabilities in complex environments while mitigating the interference of irrelevant background features. …”
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STFNet: A Spatiotemporal Fusion Network for Forest Change Detection Using Multi-Source Satellite Images by Yingjiao Tan, Kaimin Sun, Jinjiang Wei, Song Gao, Wei Cui, Yu Duan, Junyi Liu, Wanghui Zhou

“…By combining fine spatial details of high-resolution images with dynamic information from time-series images, STFNet enhances the accuracy of forest change detection, alleviating the problem of information fusion difficulties caused by inconsistent granularity in spatiotemporal spectral features from different sources. In STFNet, we propose a cross-attention-based temporal differential feature fusion module (CATFF) to capture spatiotemporal dependencies within time-series images and a multiresolution contextual differential feature fusion module (MCDF) to achieve efficient spatial contexture fusion across multiresolution images. …”
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Lightweight Neural Network for Centroid Detection of Weak, Small Infrared Targets via Background Matching in Complex Scenes by Xiangdong Xu, Jiarong Wang, Zhichao Sha, Haitao Nie, Ming Zhu, Yu Nie

“…This design significantly improves the network’s ability to extract target motion features and effectively reduces the interference of static backgrounds. …”
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YOLOv8-MFD: An Enhanced Detection Model for Pine Wilt Diseased Trees Using UAV Imagery by Hua Shi, Yonghang Wang, Xiaozhou Feng, Yufen Xie, Zhenhui Zhu, Hui Guo, Guofeng Jin

“…To further improve robustness and precision, we integrate a Focal Modulation mechanism to suppress environmental interference and adopt a Dynamic Head to strengthen multi-scale object perception and adaptive feature fusion. …”
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Progressive Guided Fusion Network With Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection by Jiajia Wu, Guangliang Han, Haining Wang, Hang Yang, Qingqing Li, Dongxu Liu, Fangjian Ye, Peixun Liu

“…Then, to strengthen the semantic expressiveness of the shallow-layer features, we design a multi-modal feature refinement mechanism (MFRM), which exploits the high-level fusion feature to guide the enhancement of the shallow-layer original RGB and depth features before they are fused. …”
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A Novel Open Circuit Fault Diagnosis for a Modular Multilevel Converter with Modal Time-Frequency Diagram and FFT-CNN-BIGRU Attention by Ziyuan Zhai, Ning Wang, Siran Lu, Bo Zhou, Lei Guo

“…Firstly, they lack the necessary feature information. Secondly, they are incapable of performing open-circuit fault diagnosis of the modular multilevel converter with the requisite degree of accuracy. …”
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A Metric Learning-Based Improved Oriented R-CNN for Wildfire Detection in Power Transmission Corridors by Xiaole Wang, Bo Wang, Peng Luo, Leixiong Wang, Yurou Wu

“…Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. …”
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WormNet: A Multi-View Network for Silkworm Re-Identification by Hongkang Shi, Minghui Zhu, Linbo Li, Yong Ma, Jianmei Wu, Jianfei Zhang, Junfeng Gao

“…Next, a feature mask module (FMM) is employed to purify the features in the spatial domain, thereby reducing the impact of background interference. …”
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GC-Faster RCNN: The Object Detection Algorithm for Agricultural Pests Based on Improved Hybrid Attention Mechanism by Bolun Guan, Yaqian Wu, Jingbo Zhu, Juanjuan Kong, Wei Dong

“…This dual attention design enables more discriminative feature extraction, which is particularly effective for distinguishing morphologically similar pest species. …”
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GANFlow: A Hybrid Model for SAR Image Target Open-Set Recognition Based on GAN and the Flow-Based Module by Jikai Qin, Jiusheng Han, Zheng Liu, Lei Ran, Rong Xie, Tat-Soon Yeo

“…Then, to find the difference in the probability density distribution of the extracted features, a flow-based module is adopted. Also, the features avoid interference from irrelevant background information in SAR images. …”
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Reading Social Policy from Polanyi’s Perspective: Problem of the Market, Wealth, and Labor by Abdülkadir Şenkal

“…Polanyi considered the relation between markets and societies as a central feature of any social order; according to him, while the market destabilizes society, the commodification of labor, land, and money creates a reaction or “counter-movement.” …”
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