GWSC-SegMamba: Gate Wavelet Spatial Convolution Enhanced State Space Model for Multi-Temporal Agricultural Land Segmentation by Yohanes Fridolin Hestrio, Aprinaldi Jasa Mantau, Wisnu Jatmiko

“…We present GWSC-SegMamba, an innovative hybrid architecture that integrates State Space Models (SSMs) with Gate Wavelet Convolution (GWC) and Gate Spatial Convolution (GSC) components to tackle multi-temporal agricultural segmentation issues. …”
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An intelligent non-destructive method to identify the quality of self-compacting concrete based on convolutional neural networks via image recognition by Zhong Xiao, Zixuan Liu, Xuying Guo, Jun Liu

“…Sampling inspection not only cannot realize the comprehensive coverage of the inspected concrete, but also is complicated, damaging and costly. An intelligent non-destructive method to identify the quality of self-compacting concrete based on convolutional neural networks (CNN) via image recognition was proposed in this study. …”
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Lightweight Stereo Matching for Real-Time Applications With 2D Cost Volume Aggregation by Thai la, Linh Tao, Dai Watanabe

“…Despite the significant advancements in learning-based stereo matching algorithms, a significant challenge remains: the high computational cost and memory demands of 3D convolutions, which hinder real-time deployment on resource-constrained platforms like edge devices. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

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Vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation. by Van-Trang Nguyen, Quoc Bao Diep

“…This article proposes a novel approach for vibration-based gearbox fault diagnosis using a multi-scale convolutional neural network with depth-wise feature concatenation named MixNet. …”
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Improved stereo matching network based on dense multi-scale feature guided cost aggregation by ZHANG Bo, ZHANG Meiling, LI Xue, ZHU Lei

“…Then, a dense multi-scale feature guided cost aggregation module was proposed, which adaptively fused the cost volume and dense multi-scale features while aggregating the cost volume, so that the subsequent decoding layers can decode more accurate and high-resolution geometry information with the guidance of multi-scale context information. …”
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Precision-Driven Semantic Segmentation of Pipe Gallery Diseases Using PipeU-NetX: A Depthwise Separable Convolution Approach by Wenbin Song, Hanqian Wu, Chunlin Pu

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Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach by Justice Williams Asare, Martin Mabeifam Ujakpa, Seth Alornyo, Emmanuel Akwah Kyei, Prince Modey, William Leslie Brown-Acquaye, Emmanuel Freeman, Alfred Coleman

“…It enhances the accuracy, efficiency, and accessibility of medical disease detection while being cost-effective and scalable, providing quicker results in healthcare screening compared to traditional methods.…”
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A Neural Network with Multiscale Convolution and Feature Attention Based on an Electronic Nose for Rapid Detection of Common Bunt Disease in Wheat Plants by Zhizhou Ren, Kun Liang, Yihe Liu, Xiaoxiao Wu, Chi Zhang, Xiuming Mei, Yi Zhang

“…This study offers a practical, rapid, and cost-effective method for monitoring and managing common bunt disease in wheat, enhancing crop protection and food security.…”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…Electroencephalography (EEG) is noninvasive, cost-effective, and has high temporal resolution, making it a promising tool for automated AD detection. …”
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Flexible hybrid edge computing IoT architecture for low-cost bird songs detection system by Francisco A. Delgado-Rajó, Carlos M. Travieso-Gonzalez

“…This study proposes a flexible hybrid edge computing IoT architecture for a low-cost bird song detection system. The system integrates low-power microcomputers, such as Raspberry Pi, equipped with USB microphones, LoRa modules, and Wi-Fi for seamless operation across rural and urban environments. …”
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Online Calibration Method of LiDAR and Camera Based on Fusion of Multi-Scale Cost Volume by Xiaobo Han, Jie Luo, Xiaoxu Wei, Yongsheng Wang

“…To solve the above problems, we propose an online calibration algorithm based on multi-scale cost volume fusion. First, a multi-layer convolutional network is used to downsample and concatenate the camera RGB data and LiDAR point cloud data to obtain three-scale feature maps. …”
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Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters by Jiuxiao Zhao, Jianping Tao, Shirui Zhang, Jingjing Li, Teng Li, Feifei Shan, Wengang Zheng

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Anomaly Detection and Remaining Useful Life Prediction for Turbofan Engines with a Key Point-Based Approach to Secure Health Management by Yuntao Duan, Tao Zhang, Dunhuang Shi

“…The research method is based on convolution and the basic shape of convolution. Through feature fusion, a self-convolution operation, a half operation, and derivative operation on the original feature data of the engine, two key points of the engine in the entire lifecycle are obtained, and these key points are analyzed in detail. …”
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Lightweight interactive feature inference network for single-image super-resolution by Li Wang, Xing Li, Wei Tian, Jianhua Peng, Rui Chen

“…Abstract The emergence of convolutional neural network (CNN) and transformer has recently facilitated significant advances in image super-resolution (SR) tasks. …”
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Toward improving precision and complexity of transformer-based cost-sensitive learning models for plant disease detection by Manh-Tuan Do, Manh-Hung Ha, Duc-Chinh Nguyen, Oscal Tzyh-Chiang Chen, Oscal Tzyh-Chiang Chen

“…Such an integration acted as regular convolutional layers that subsequently substituted for the original layers to cut computational costs. …”
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Shuffle window transformer DeepLabV3+: a lightweight convolutional neural network and transformer based hybrid semantic segmentation network by Yane Li, Zhichao Chen, Hongxia Qi, Ming Fan, Lihua Li

“…To address this, this paper proposes a semantic segmentation network based on a hybrid architecture of convolutional neural network and Transformer, named shuffle window transformer DeeplabV3+ (SWT-DeepLabV3+). …”
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Swin-ReshoUnet: A Seismic Profile Signal Reconstruction Method Integrating Hierarchical Convolution, ORCA Attention, and Residual Channel Attention Mechanism by Jie Rao, Mingju Chen, Xiaofei Song, Chen Xie, Xueyang Duan, Xiao Hu, Senyuan Li, Xingyue Zhang

“…The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. …”
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Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…This enables manufacturers to make informed decisions in real time, enhancing product consistency, minimizing defects, and optimizing process parameters for improved productivity and cost-effectiveness.…”
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Unobtrusive Sleep Posture Detection Using a Smart Bed Mattress with Optimally Distributed Triaxial Accelerometer Array and Parallel Convolutional Spatiotemporal Network by Zhuofu Liu, Gaohan Li, Chuanyi Wang, Vincenzo Cascioli, Peter W. McCarthy

“…Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. …”
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