Context-Adaptable Deployment of FastSLAM 2.0 on Graphic Processing Unit with Unknown Data Association by Jessica Giovagnola, Manuel Pegalajar Cuéllar, Diego Pedro Morales Santos

“…The parallelization process involves identifying the parameters affecting the computational complexity in order to distribute the computation among single multiprocessors as efficiently as possible. …”
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TongueNet: a multi-modal fusion and multi-label classification model for traditional Chinese Medicine tongue diagnosis by Lijuan Yang, Lijuan Yang, Qiumei Dong, Da Lin, Xinliang Lü

“…Moreover, TongueNet contains only 32.1 M parameters, significantly reducing computational resource requirements while maintaining high diagnostic performance. …”
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GSF-YOLOv8: A Novel Approach for Fire Detection Using Gather-Distribute Mechanism and SimAM Attention by Caixiong Li, Dali Wu, Xing Zhang, Peng Wu

“…To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. …”
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A lightweight deep-learning model for parasite egg detection in microscopy images by Wenbin Xu, Qiang Zhai, Jizhong Liu, Xingyu Xu, Jing Hua

“…Different from the FPN structure, which mainly integrates semantic feature information at adjacent levels, the hierarchical and asymptotic aggregation structure of AFPN can fully fuse the spatial contextual information of egg images, and its adaptive spatial feature fusion mode can help the model select beneficial feature and ignore redundant information, thereby reducing computational complexity and improving detection performance. …”
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An Experimental Study of the Gemination in Arabic Language by Kamel FERRAT, Mhania GUERTI

“…To extract the feature characteristics, we have carried out an acoustic analysis by computing the values of frequency formants, energy and durations of the consonants and subsequent vowels in the various [VCV] and [VCgV] utterances (Cg: geminate consonant). …”
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Synergistic Multi-Model Approach for GPR Data Interpretation: Forward Modeling and Robust Object Detection by Hang Zhang, Zhijie Ma, Xinyu Fan, Feifei Hou

“…Ground penetrating radar (GPR) is widely used for subsurface object detection, but manual interpretation of hyperbolic features in B-scan images remains inefficient and error-prone. …”
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A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis by Jiamao Yu, Hexuan Hu

“…However, current deep learning-based methods face significant challenges, particularly due to the scarcity of fault data, which impedes the models’ ability to effectively learn parameters. Additionally, many existing methods rely on single-scale features, hindering the capture of global contextual information and diminishing diagnostic accuracy. …”
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Research on road surface damage detection based on SEA-YOLO v8. by Yuxi Zhao, Baoyong Shi, Xiaoguang Duan, Wenxing Zhu, Liying Ren, Chang Liao

“…Firstly, the SBS module is constructed to optimize the computational complexity, achieve real-time target detection under limited hardware resources, successfully reduce the model parameters, and make the model more lightweight; Secondly, we integrate the EMA attention mechanism module into the neck component, enabling the model to utilize feature information from different layers, enabling the model to selectively focus on key areas and improve feature representation; Then, an adaptive attention feature pyramid structure is proposed to enhance the feature fusion capability of the network; Finally, lightweight shared convolutional detection head (LSCD-Head) is introduced to improve feature representation and reduce the number of parameters. …”
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PS-SLAM: A Visual SLAM for Semantic Mapping in Dynamic Outdoor Environment Using Panoptic Segmentation by Gang Li, Jinxiang Cai, Chen Huang, Hao Luo, Jian Yu

“…In addition, we use a stereo matching model to generate disparity images and compute depth information based on camera parameters. …”
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FDM-RTDETR: A Multi-Scale Small Target Detection Algorithm by Hongya Wang, Yongtao Yu, Zhaoxia Tang

“…While maintaining low computational complexity and parameter count, the method demonstrates significant improvements in detection performance. …”
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Meat analogues: The relationship between mechanical anisotropy, macrostructure, and microstructure by Miek Schlangen, Iris van der Doef, Atze Jan van der Goot, Mathias P. Clausen, Thomas E. Kodger

“…The relationship between microstructural air bubbles and macrostructure and mechanical properties is apparent in all correlation analyses. Last, univariate feature selection provided insight into which parameters are most important for selected target features.…”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

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Study on lightweight strategies for L-YOLO algorithm in road object detection by Ji Hong, Kuntao Ye, Shubin Qiu

“…This modification improves the capture of features and contextual information for small vehicle targets without significantly increasing computational demands. …”
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TCSR: Lightweight Transformer and CNN Interaction Network for Image Super-Resolution by Danlin Cai, Wenwen Tan, Feiyang Chen, Xinchi Lou, Jianbin Xiahou, Daxin Zhu, Detian Huang

“…Recent Transformer has attracted increasing attention in lightweight SR methods owing to its remarkable global feature extraction capacity. However, the huge computational cost makes it challenging for lightweight SR methods to efficiently utilize Transformer to exploit global contextual information from shallow to intermediate layers. …”
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Intelligent deep learning architecture for precision vegetable disease detection advancing agricultural new quality productive forces by Jun Liu, Xuewei Wang, Qian Chen, Peng Yan, Dugang Guo

“…The Adaptive Detail Enhancement Convolution (ADEConv) module employs dynamic parameter adjustment to preserve fine-grained features while maintaining computational efficiency. …”
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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

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ConvTransNet-S: A CNN-Transformer Hybrid Disease Recognition Model for Complex Field Environments by Shangyun Jia, Guanping Wang, Hongling Li, Yan Liu, Linrong Shi, Sen Yang

“…This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. …”
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RDCU-Net: A Multi-Scale Residual Dilated Convolution U-Net with Spatial Pyramid Pooling for Brain Tumor Segmentation by Mohammad Soltani-Gol, Akbar Asgharzadeh-Bonab, Hamid Soltanian-Zadeh, Jalil Mazlum

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Rumor Detection Based on Knowledge Enhancement and Graph Attention Network by Wanru Wang, Yuwei Lv, Yonggang Wen, Xuemei Sun

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Lightweight and robust ship detection method driven by self-attention mechanism by Feng MA, Zihui SHI, Jie SUN, Chen CHEN, Xianbin MAO, Xinping YAN

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