Speech Competencies in Russian-Speaking Monolinguals of the Tyva Republic in the Context of Tuvan-Russian Bilingualism by Evgeny Y. Litskevich

Single-Character-Based Embedding Feature Aggregation Using Cross-Attention for Scene Text Super-Resolution by Meng Wang, Qianqian Li, Haipeng Liu

“…However, the ambiguity of character regions in complex backgrounds remains challenging to mitigate, particularly the interference between tightly connected characters. In this paper, we propose single-character-based embedding feature aggregation using cross-attention for scene text super-resolution (SCE-STISR) to solve this problem. …”
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DMSF-YOLO: Cow Behavior Recognition Algorithm Based on Dynamic Mechanism and Multi-Scale Feature Fusion by Changfeng Wu, Jiandong Fang, Xiuling Wang, Yudong Zhao

“…The model can suppress the interference of background information, dynamically extract multi-scale features, perform feature fusion, distinguish similar behaviors of cows, enhance the capacity to detect small targets, and significantly improve the recognition accuracy and overall performance of the model. …”
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Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction by Yanchang LV, Jingyue Wang, Chengqiang Zhang, Jianming Ding

“…The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.…”
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Modulation Signal Bispectrum Slice Overall Average Feature Extraction of Low-speed Reciprocating Bearing Faults by Zhang Hao, Hu Lei, Xu Yuandong, Hu Niaoqing

“…Finally, an overall average of the MSB modulation signal slice spectra from the short signal collection results in the overall averaging feature. Validation of the method using fault test data demonstrates its effectiveness in diagnosing faults in low-speed reciprocating motion bearings.…”
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MCRS-YOLO: Multi-Aggregation Cross-Scale Feature Fusion Object Detector for Remote Sensing Images by Lu Liu, Jun Li

“…Finally, the Normalized Wasserstein Distance (NWD) is introduced into hybrid loss training to emphasize small object features and suppress background interference. The efficacy and superiority of MCRS-YOLO are rigorously validated through extensive experiments on two publicly available datasets: NWPU VHR-10 and VEDAI. …”
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Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms by Yiheng Qian, Zhiyong Xiao, Zhaohong Deng

“…This paper proposes a novel deep-learning architecture for crop pest classification, addressing the limitations of existing methods that struggle with distinguishing the fine details of pests and background interference. The proposed model is designed to balance fine-grained feature extraction with deep semantic understanding, utilizing a parallel structure composed of two main components: the Feature Fusion Module (FFM) and the Mixed Attention Module (MAM). …”
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Intelligent robot chair with communication aid using TEP responses and higher order spectra band features by Sathees Kumar Nataraj, Paulraj Murugesa Pandiyan, Sazali Bin Yaacob, Abdul Hamid bin Adom

“…The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. …”
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MSF-SLAM: Enhancing Dynamic Visual SLAM with Multi-Scale Feature Integration and Dynamic Object Filtering by Yongjia Duan, Jing Luo, Xiong Zhou

“…Conventional visual SLAM systems often struggle with degraded pose estimation accuracy in dynamic environments due to the interference of moving objects and unstable feature tracking. …”
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Radar-Based Hand Gesture Recognition With Feature Fusion Using Robust CNN-LSTM and Attention Architecture by Irshad Khan, Young-Woo Kwon

“…Additionally, attention mechanisms enhance feature selection, ultimately improving recognition performance. …”
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Rotation-Invariant Feature Enhancement with Dual-Aspect Loss for Arbitrary-Oriented Object Detection in Remote Sensing by Zhao Hu, Xiangfu Meng, Xinsong Liu, Zhuxiang Sun

“…Specifically, we introduce a rotation-invariant learning (RIL) module, which employs adaptive rotation transformations to enhance shallow feature representations, thereby effectively mitigating interference from complex backgrounds and boosting geometric robustness. …”
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Agricultural Greenhouse Extraction Based on Multi-Scale Feature Fusion and GF-2 Remote Sensing Imagery by Yuguang Chang, Xiaoyu Yu, Xu Yang, Zhengchao Chen, Pan Chen, Xuan Yang, Yongqing Bai

“…However, the dense spatial distribution, irregular morphology, and complex background interference of greenhouses often limit the effectiveness of conventional segmentation methods. …”
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DiffMamba: semantic diffusion guided feature modeling network for semantic segmentation of remote sensing images by Zhen Wang, Nan Xu, Zhuhong You, Shanwen Zhang

“…EPSM focuses on enhancing local feature representation in the channel dimension, using the phase information of object region features to improve local information interaction and filter out clutter noise interference. …”
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Near-random connections support top-down feature-based attentional modulations in early sensory cortex. by Sunyoung Park, John T Serences

“…However, the neurons in PFC that are thought to provide top-down feedback typically have high-dimensional tuning for multiple features, so it is unclear how feedback selectively modulates responses in neurons tuned to a relevant stimulus without incidentally causing interference by co-modulating neurons tuned to irrelevant features. …”
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SAVL: Scene-Adaptive UAV Visual Localization Using Sparse Feature Extraction and Incremental Descriptor Mapping by Ganchao Liu, Zhengxi Li, Qiang Gao, Yuan Yuan

“…Firstly, to tackle the challenge of inaccurate localization resulting from sparse terrain features, this work proposes a novel feature extraction network grounded in a general visual model, leveraging the robust zero-shot generalization capability of the pre-trained model and extracting sparse features from UAV and satellite imagery. …”
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YOLOv10n-Based Defect Detection in Power Insulators: Attention Enhancement and Feature Fusion Optimization by Zhihao Wei, Yan Wei

“…The spatial information of the channels is compressed by global average pooling, and the feature map is adaptively weighted after learning the channel weights through the fully connected layer, so as to strengthen the key channel features and suppress the noise. …”
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Multi-Level Feature Dynamic Fusion Neural Radiance Fields for Audio-Driven Talking Head Generation by Wenchao Song, Qiong Liu, Yanchao Liu, Pengzhou Zhang, Juan Cao

“…Then, we introduce the idea of multi-head attention and design an efficient audio-visual fusion module that explicitly fuses audio features with image features from different planes, thereby improving the mapping between audio features and spatial information. …”
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A Rotated Object Detection Model With Feature Redundancy Optimization for Coronary Athero-Sclerotic Plaque Detection by Xue Hao, Haza Nuzly Abdull Hamed, Qichen Su, Xin Dai, Linqiang Deng

“…These redundant features interfere with plaque feature extraction, resulting in decreased performance and increased computational complexity. …”
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Time-Frequency Feature Extraction Method for Weak Acoustic Signals from Drill Pipe of Seafloor Drill by Jingwei Xu, Buyan Wan, Weicai Quan, Yi Xi, Xianglin Tian

“…The acoustic signals of the drill pipe of a seafloor drill present weak features under noise interference such as marine environmental noise and the mechanical vibration of the seafloor drill. …”
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A seed-epidermis-feature-recognition-based lightweight peanut seed selection method for embedded systems by Dehao Li, Jinlong Huang, Xincheng Li, Zhaolei Yang, Xueke An, Pengfei Xu, Yuliang Yun

“…The transfer learning method was used to use four pre-trained models, EfficientNet_b0, EfficientNetv2-b0, MobileNet_v2_35_224, and NasNet_Mobile, as feature extraction layers, the input layer was added before the feature extraction layer, and the dropout and dense layers were added after the feature extraction layer to construct a classifier. …”
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