Central Pixel-Based Dual-Branch Network for Hyperspectral Image Classification by Dandan Ma, Shijie Xu, Zhiyu Jiang, Yuan Yuan

“…To address these issues, we propose a central pixel-based dual-branch network (CPDB-Net) that synergistically integrates CNN and ViT for robust feature extraction. Specifically, the central spectral feature extraction branch based on CNN serves as a strong prior to reinforce the importance of central pixel features in classification. …”
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A Small-Sample Target Detection Method for Transmission Line Hill Fires Based on Meta-Learning YOLOv11 by Yaoran Huo, Yang Zhang, Jian Xu, Xu Dai, Luocheng Shen, Conghong Liu, Xia Fang

“…After this, the re-weighting module learns class-specific re-weighting vectors from the support set samples and uses them to recalibrate the mapping of meta-features. To enhance the model’s ability to learn target hill fire features from complex backgrounds, adaptive feature fusion (AFF) is integrated into the feature extraction process of YOLOv11 to improve the model’s feature fusion capabilities, filter out useless information in the features, and reduce the interference of complex backgrounds in detection. …”
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Class-Discrepancy Dynamic Weighting for Cross-Domain Few-Shot Hyperspectral Image Classification by Chen Ding, Jiahao Yue, Sirui Zheng, Yizhuo Dong, Wenqiang Hua, Xueling Chen, Yu Xie, Song Yan, Wei Wei, Lei Zhang

“…It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. …”
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MC-ASFF-ShipYOLO: Improved Algorithm for Small-Target and Multi-Scale Ship Detection for Synthetic Aperture Radar (SAR) Images by Yubin Xu, Haiyan Pan, Lingqun Wang, Ran Zou

“…Despite remarkable advances in deep-learning-based detection methods, performance remains constrained by the vast size differences between ships, limited feature information of small targets, and complex environmental interference in SAR imagery. …”
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Automatic picking method for ground penetrating radar wave groups at rough coal-rock interfaces by Ying TIAN, Chunzhi LI, Shuo CHEN, Zihao WANG, Fuyan LYU, Qiang ZHANG, Meng HAN, Chengjun HU

“…The method also employs a RANSAC iterative fitting algorithm and waveform feature matching to classify and identify interfering hyperbolas and coal-rock interface curves. …”
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Improving YOLOv11 for marine water quality monitoring and pollution source identification by Fang Wang

“…Additionally, Multi-scale Feature Fusion (MFF) combines Convolutional Neural Networks (CNN) and Transformer-based feature extraction to enhance robustness in complex environments. …”
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Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow by Shu Dai, Lei Liang, Ke Jiang, Hui Wang, Chengyi Zhong

“…It proposes a dispersion feature enhancement algorithm based on cross-correlation, which combines a rotatable elliptical template with normalized cross-correlation coefficients to suppress interference from non-target directions. …”
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AAV Parameters Estimation Based on Improved Time-Frequency Ridge Extraction and Hough Transform by Yongji Yu, Yonghong Ruan, Junjie Zhong

“…However, existing methods are prone to noise interference and exhibit poor performance in extracting multi-rotor and multi-component signals. …”
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IR-ADMDet: An Anisotropic Dynamic-Aware Multi-Scale Network for Infrared Small Target Detection by Ning Li, Daozhi Wei

“…This framework optimally refines features across multiple scales. The entire architecture is optimized for efficiency using dynamic feature recalibration. …”
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A probabilistic neural network-based bimanual control method with multimodal haptic perception fusion by Xinrui Chi, Zhanbin Guo, Fu Cheng

“…A hierarchical heterogeneous feature alignment (HHFA) module is designed to solve the spatio-temporal asynchrony of multi-source signals (root mean square error <0.8 ms), and a dynamic Bayesian fusion layer (DBFL) is developed to achieve adaptive weighting based on the entropy-variance coupling index, suppressing noise interference and modal conflicts. …”
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Capacity Prognostics of Marine Lithium-Ion Batteries Based on ICPO-Bi-LSTM Under Dynamic Operating Conditions by Qijia Song, Xiangguo Yang, Telu Tang, Yifan Liu, Yuelin Chen, Lin Liu

“…First, the battery is simulated according to the actual operating conditions of an all-electric ferry, and in each charge/discharge cycle, the sum, mean, and standard deviation of each parameter (current, voltage, energy, and power) during battery charging, as well as the voltage difference before and after the simulated operating conditions, are calculated to extract a series of features that capture the complex nonlinear degradation tendency of the battery, and then a correlation analysis is performed on the extracted features to select the optimal feature set. …”
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A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement by Liquan Zhao, Yuda Li, Tie Zhong

“…Next, we design a multi-scale dilated convolution module to capture underwater features at different scales. Then, we design a feature fusion adaptive attention module to reduce the interference of redundant features and enhance the local perception capabilities. …”
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A marine ship detection method for super-resolution SAR images based on hierarchical multi-scale Mask R-CNN by Jiancong Fan, Miaoxin Guo, Lei Zhang, Jianjun Liu, Jianjun Liu, Yang Li, Yang Li

“…However, their inherent low resolution, scattered noise, and complex background interference severely limit the accuracy of target detection. …”
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NAS-YOLOX: a SAR ship detection using neural architecture search and multi-scale attention by Hao Wang, Dezhi Han, Mingming Cui, Chongqing Chen

“…However, SAR-based ship detection suffers from limitations such as strong scattering of targets, multiple scales, and background interference, leading to low detection accuracy. To address these limitations, this paper presents a novel SAR ship detection method, NAS-YOLOX, which leverages the efficient feature fusion of the neural architecture search feature pyramid network (NAS-FPN) and the effective feature extraction of the multi-scale attention mechanism. …”
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MEAC: A Multi-Scale Edge-Aware Convolution Module for Robust Infrared Small-Target Detection by Jinlong Hu, Tian Zhang, Ming Zhao

“…Infrared small-target detection remains a critical challenge in military reconnaissance, environmental monitoring, forest-fire prevention, and search-and-rescue operations, owing to the targets’ extremely small size, sparse texture, low signal-to-noise ratio, and complex background interference. Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. …”
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Application of сolored decorative coatings on structural steels surface by Semen M. Lipkin, Svetlana V. Kucherenko, Irina Y. Zhukova, Maria V. Kolchina

“…Staining copper organic compounds with aqueous solutions has the following feature: the coating color varies depending on the resistance time in solution, which is associated with changing in thickness of the forming oxide layer where interference phenomena occur. …”
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MSTCNet: Toward Generalization Improving for Multiframe Infrared Small Target Detection by Ruining Cui, Na Li, Junfu Liu, Huijie Zhao

“…This module enhances domain-invariant infrared small target features and reduces the impact of other irrelevant interferences. …”
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Acquisition versus consolidation of auditory perceptual learning using mixed-training regimens. by David W Maidment, HiJee Kang, Emma C Gill, Sygal Amitay

“…Based on previous literature we predicted that acquisition would be disrupted by varying the task-relevant stimulus feature during training (stimulus interference), and that consolidation would be disrupted by varying the perceptual judgment required (task interference). …”
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Aging monitoring and fault positioning for zinc oxide surge arresters based on the fifth harmonic of the leakage current by Yongling Lu, Zhitong Xue, Jiahao Guo, Chenyu Zhang, Jian Liu, Xiaolong Xiao, Jian Sun

“…In addition, it builds an aging experimental platform for zinc oxide surge arresters with voltage harmonic interference. An improved displacement current method and a fast Fourier transform algorithm are used to extract current harmonic features, and the percentage changes in features and harmonic sensitivity are introduced to analyze the fifth harmonic characteristics. …”
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DAFDM: A Discerning Deep Learning Model for Active Fire Detection Based on Landsat-8 Imagery by Xu Gao, Wenzhong Shi, Min Zhang, Lukang Wang

“…Traditional methods for detecting AFs rely on the statistical analysis of AF radiance and background features. However, these algorithms are resource-intensive to develop and exhibit limited adaptability, particularly in distinguishing AF from interference pixels. …”
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