The TDGL Module: A Fast Multi-Scale Vision Sensor Based on a Transformation Dilated Grouped Layer by Leilei Xie, Fenghua Zhu, Zhixue Wang

“…Traditional spatial pyramid pooling methods fuse multi-scale feature information but lack adaptability in dynamically adjusting convolution operations based on their actual needs. …”
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Reversible image steganography based on residual structure and attention mechanism by Lianshan Liu, Shanshan Tong, Qianwen Xue

“…The network uses INN as the overall framework, adopts a double-branch structure, extracts deep features using the mixed attention mechanism, and employs channel shuffle to promote information interaction between different features. This paper introduces dilated convolution to design a multi-scale convolution attention module that combines feature information from different scales, highlights essential features, and precisely locates the ideal embedding position. …”
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Multiscale Spatial-Spectral CNN-Transformer Network for Hyperspectral Image Super-Resolution by Jiayang Zhang, Hongjia Qu, Junhao Jia, Yaowei Li, Bo Jiang, Xiaoxuan Chen, Jinye Peng

“…However, these methods predominantly focus on capturing local features using convolutional neural networks (CNNs), neglecting the comprehensive utilization of global spatial-spectral information. …”
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FPGA-oriented lightweight multi-modal free-space detection network by Feiyi Fang, Junzhu Mao, Wei Yu, Jianfeng Lu

“…With the development of multi-modal convolutional neural networks (CNNs) in recent years, the performance of driving scene semantic segmentation algorithms has been dramatically improved. …”
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BCSM-YOLO: An Improved Product Package Recognition Algorithm for Automated Retail Stores Based on YOLOv11 by Pingqing Hou, Shaoze Huang

“…Then, the Convolutional Block Attention Module (CBAM) screens the processed data, adaptively focuses on the key regions, and suppresses the background interference. …”
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Underwater Time Delay Estimation Based on Meta-DnCNN with Frequency-Sliding Generalized Cross-Correlation by Meiqi Ji, Xuerong Cui, Juan Li, Lei Li, Bin Jiang

“…Then, the grayscale image corresponding to the generated FS-GCC matrix is used, and the multi-level noise features are extracted by the multi-layer convolution of denoising convolutional neural network (DnCNN), effectively suppressing the noise and improving the estimation accuracy. …”
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Early stroke behavior detection based on improved video masked autoencoders for potential patients by Meng Wang, Guanci Yang, Kexin Luo, Yang Li, Ling He

“…On the basis of establishing the masking mechanism for adjacent frames and pixel blocks within these sequences, The EPBR-PS employes pipeline mask strategy to extract spatiotemporal features effectively. Then, the local convolution attention mechanism is designed to capture local dynamic feature information, and central to the EPBR-PS is the integration of local convolutional attention mechanism with VideoMAE's multi-head attention mechanism. …”
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Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns by Ziyi Sun, Bing Xue, Mengjie Zhang, Jan Schindler

“…Instance segmentation techniques based on convolutional neural networks (CNNs) is a vital tool for accurately identifying and segmenting individual tree crowns, which plays an essential role in environmental monitoring and forest management. …”
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Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation by Yangfan Luo, Jiuxiang Dai, Shenye Shi, Yuanjun Xu, Wenqi Zou, Haojia Zhang, Xiaonan Yang, Zuoxi Zhao, Yuanhong Li

“…Different semantic segmentation models are trained and tested using low altitude high-resolution images of drones, and compared. …”
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Research on the Classification Method of Tea Tree Seeds Quality Based on Mid-Infrared Spectroscopy and Improved DenseNet by Di Deng, Hao Li, Jiawei Luo, Jiachen Jiang, Hongbo Mu

“…Based on DenseNet121, the Batch Channel Normalization (BCN) module was introduced to reduce the dimensionality via 1 × 1 convolution while preserving the feature extraction capabilities, the Attention–Convolution Mix (ACMix) module was integrated to combine convolution and self-attention, and the Efficient Channel Attention (ECA) mechanism was utilized to enhance the feature discriminability. …”
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A Study on Energy Consumption in AI-Driven Medical Image Segmentation by R. Prajwal, S. J. Pawan, Shahin Nazarian, Nicholas Heller, Christopher J. Weight, Vinay Duddalwar, C.-C. Jay Kuo

“…To address these aspects, we evaluated three variants of convolution—Standard Convolution, Depthwise Convolution, and Group Convolution—combined with optimization techniques such as Mixed Precision and Gradient Accumulation. …”
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Reduction of Electromagnetic Reflections in 3D Airborne Transient Electromagnetic Modeling: Application of the CFS-PML in Source-Free Media by Yanju Ji, Xuejiao Zhao, Jiayue Gu, Dongsheng Li, Shanshan Guan

“…To solve the problem of electromagnetic reflections caused by the termination of finite-difference time-domain (FDTD) grids, we apply the complex frequency-shifted perfectly matched layer (CFS-PML) to airborne transient electromagnetic (ATEM) modeling in a source-free medium. …”
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HRNet Encoder and Dual-Branch Decoder Framework-Based Scene Text Recognition Model by Meiling Li, Xiumei Li, Junmei Sun, Yujin Dong

“…In the decoder module, the dual-branch structure is adopted, in which the super-resolution branch takes the feature maps with the highest resolution obtained in the encoder module as input and restores images by upsampling through transposed convolution. The four kinds of feature maps with different resolutions are fused through independent transposed convolution layers for multiscale fusion in the recognition branch and then inputted into the attention-based decoder for text recognition. …”
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Radio frequency based distributed system for noncooperative UAV classification and positioning by Chaozheng Xue, Tao Li, Yongzhao Li

“…After the UAV signal is detected, the time difference of arrival (TDOA) of the UAV signal arriving at the receiver is estimated by the cross-correlation method to obtain the corresponding distance difference. …”
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Learner preferences prediction with mixture embedding of knowledge and behavior graph by Xiaoguang LI, Lei GONG, Xiaoli LI, Xin ZHANG, Ge YU

“…To solve the problems of inaccurate prediction of learner preference and insufficient utilization of structural information in the knowledge recommendation model, for the knowledge structure and learner behavior structure in the learner’s preference prediction model, the model of learner preferences predication with mixture embedding of knowledge and behavior graph was proposed.First, considering using graph convolution network (GCN) to fit structural information, GCN was extended to knowledge graph and behavior graph, the purpose of which was to obtain learners’ overall learning pattern and individual learning pattern.Then, the difference between knowledge structure and behavior structure was used to fit learners’ individual preferences, and recurrent neural network was used to encode and decode learners’ preferences to obtain the distribution of learners’ preference distribution.The experimental results on the real datasets demonstrate that the proposed model has a good effect on predicting learner preferences.…”
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Research on lightweight non-intrusive load disaggregation model for edge computing by YE Canshen, LUO Dehan, HE Jiafeng

“…In addition, the design of different decoders is investigated in this paper, and the depthwise separable convolution is used to improve the residual block in the upsampling layer and reduce the number of kernels in the convolution layer, so that the model has fewer parameters and requires less computation while ensuring good load disaggregation performance. …”
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Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining by Shenyu Chen, Meng Li, Weifeng Yang

“…In this paper, we propose a novel graph convolution network model, named multilevel feature fusion-based graph convolution network (MFF-GCN) which can employ multiple streams of GCNs to learn different level features of rumor data, respectively. …”
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An FPGA-Based Hardware Accelerator for CNNs Using On-Chip Memories Only: Design and Benchmarking with Intel Movidius Neural Compute Stick by Gianmarco Dinelli, Gabriele Meoni, Emilio Rapuano, Gionata Benelli, Luca Fanucci

“…During the last years, convolutional neural networks have been used for different applications, thanks to their potentiality to carry out tasks by using a reduced number of parameters when compared with other deep learning approaches. …”
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Video Visualization Technology and Its Application in Health Statistics Teaching for College Students by Chengfei Li, Yuan Xie, Shuanbao Li

“…In view of the present situation of “learning difficulty” in health statistics, this paper proposes a video visualization technology based on the convolutional neural network, which updates parameters by calculating the gradient of loss function to obtain accurate or nearly accurate loss function. …”
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Deep learning for predicting myopia severity classification method by WangMeiYu Xing, XiaoNa Li, JingShu Ni, YuanZhi Zhang, ZhongSheng Li, Yong Liu, YiKun Wang, Yao Huang

“…To improve the efficiency of myopia screening, this paper proposes a deep learning model, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to classify different severities of myopia. …”
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