LRA-UNet: A Lightweight Residual Attention Network for SAR Marine Oil Spill Detection by Yu Cai, Jingjing Su, Jun Song, Dekai Xu, Liankang Zhang, Gaoyuan Shen

“…Our model integrates depthwise separable convolutions to reduce feature redundancy and computational cost, while adopting a residual encoder enhanced with the Simple Attention Module (SimAM) to improve the precise extraction of target features. …”
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PERFORMANCE ANALYSIS FOR DIRECTION-FINDING CIRCULAR ANTENNA ARRAY by Svyatoslav V. Ballandovich, Grigory A. Kostikov, Liubov M. Liubina, Mikhail I. Sugak

“…Despite their widespread use, a number of significant issues is underinvestigated. Among them are frequency dependence of the antenna factor (the ratio of the electric field intensity module to the voltage amplitude at the load connected to the output terminals) of the circular antenna array elements in the correct electrodynamic setting. …”
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MPE-HRNet^<i>L</i>: A Lightweight High-Resolution Network for Multispecies Animal Pose Estimation by Jiquan Shen, Yaning Jiang, Junwei Luo, Wei Wang

“…Secondly, we construct a feature extraction module based on a mixed pooling module and a dual spatial and channel attention mechanism, and take the feature extraction module as the basic module of MPE-HRNet<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mphantom><mo>.…”
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Benthos-DETR: a high-precision efficient network for benthic organisms detection by Weibo Rao, Gang Chen, Yifei Zhang, Jue Cang, Shusen Chen, Chenyang Wang

“…In the neck, the original concatenation module is replaced with the Fusion Focus Module, effectively aggregating feature layer information from different stages of the backbone to achieve cross-scale feature fusion. …”
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YOLO-GCOF: A Lightweight Low-Altitude Drone Detection Model by Wanjun Yu, Kongxin Mo

“…YOLO-GCOF incorporates the GSConv-Integrated Dynamic Group Convolution Shuffle Transformer (GI-DGCST) module as the feature extraction module, which captures fine-grained details and improves the detection of small-scale features. …”
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Research on Technology of Optical Switch Devices with Integrated Tunable Filters by HU Di, XIAO Qingming, ZHENG Jie

“…To reduce the implementation complexity of the ROADM downstream transmission system, it is necessary to address the issue of bulky module sizes. Therefore, this article designs an OSW device integrated with TOF functionality to reduce the size of the MCS module and optimize its structure.…”
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Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment by Jian Zhu, Qian Li, Shi Ying, Zhihua Zheng

“…Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Firstly, a lightweight ADown module was incorporated to replace the conventional stride-2 convolution. …”
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MedFuseNet: fusing local and global deep feature representations with hybrid attention mechanisms for medical image segmentation by Ruiyuan Chen, Saiqi He, Junjie Xie, Tao Wang, Yingying Xu, Jiangxiong Fang, Xiaoming Zhao, Shiqing Zhang, Guoyu Wang, Hongsheng Lu, Zhaohui Yang

“…For feature fusion and enhancement, the designed hybrid attention mechanisms combine four different attention modules: (1) an atrous spatial pyramid pooling (ASPP) module for the CNN branch, (2) a cross attention module in the encoder for fusing local and global features, (3) an adaptive cross attention (ACA) module in skip connections for further performing fusion, and (4) a squeeze-and-excitation attention (SE-attention) module in the decoder for highlighting informative features. …”
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Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration by Haolin Chang, Zhihua Xie, Fan Yang

“…Specifically, to obtain cross-modal complementary information, this scheme sequentially constructs a Multi-Scale Feature Extraction Module (MFEM) and a Multi-scale hierarchical Semantic-Guided Fusion Module (MSGFM). …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…Firstly, the FPC-F (Fast Parallel Cross-Fusion) module, which incorporates PConv, and the S-EMCP (Space-efficient Merging Convolution Pooling) module are designed in the backbone network to replace C2F and SPPF at various scale branches. …”
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Multi dynamic temporal representation graph convolutional network for traffic flow prediction by Zuojun Wu, Xiaojun Liu, Xiaoling Zhang

“…Moreover, a multiaspect fusion module is presented, which combines auxiliary hidden states learned from traffic volume with primary hidden states derived from traffic speed. …”
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SOD-YOLO: A lightweight small object detection framework by Yunze Xiao, Nan Di

“…The DSDM-LFIM backbone network, which combines Deep-Shallow Downsampling Modules (DSD Modules) and Lightweight Feature Integration Modules (LFI Modules), avoids excessive use of group convolutions and element-wise operations. …”
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Fusion of Masked Autoencoder for Adaptive Augmentation Sequential Recommendation by SUN Xiujuan, SUN Fuzhen, LI Pengcheng, WANG Aofei, WANG Shaoqing

“…In order to address the issue of poor-quality contrast views generated by contrastive learning methods in sequential recommendation tasks, a model called GATSR, which is based on graph attention networks for sequential recommendation, is proposed. …”
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Improved image reconstruction from brain activity through automatic image captioning by Fatemeh Kalantari, Karim Faez, Hamidreza Amindavar, Soheila Nazari

“…Our proposed method consists of two main modules: visual reconstruction and semantic reconstruction. …”
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EML-SlowFast: A behavior recognition model for lion-head goose by Jinwei Wang, Zhiguo Du, Bin Wen, Zhihui Wu, Xudong Lin

“…The Efficient Channel Attention Bottleneck (ECAbneck) module and the Large Kernel Global-Local Feature Extraction (LGLE) module are designed and incorporated into the model. …”
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YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection by Yuxi Huang, Hong Zhao, Jie Wang

“…Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. …”
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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

“…Firstly, a Graph Convolutional Network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. …”
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User Experiences Among Patients and Health Care Professionals Who Participated in a Randomized Controlled Trial of E-nergEYEze, a Vision-Specific eHealth Intervention to Reduce Fat... by Manon HJ Veldman, Hilde PA van der Aa, Hans Knoop, Christina Bode, Ger HMB van Rens, Ruth MA van Nispen

“…ResultsE-nergEYEze was completed by 63% (32/51) of patients for more than 80% of the module steps. Overall, results on user engagement showed that a median 89% (IQR 45%-100%) of all assigned module steps were completed, with all modules being completed by at least 50% (37/51) of the patients. …”
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Effective Land Use Classification Through Hybrid Transformer Using Remote Sensing Imagery by Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, Anwaar Ul-Haq, David Blake, Naeem Janjua

“…The technique comprises three key components: a spectral-spatial convolutional module (SSCM), a spatial attention module (SAM), and a transformer module (TM). …”
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