Predicting the bounds of large chaotic systems using low-dimensional manifolds. by Asger M Haugaard

“…This is important, since the computational cost associated with discretised manifolds depends exponentially on their dimension. …”
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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

“…These optimized features are used for training a multi-class support vector machine, with its parameters further optimized for enhanced classification accuracy. …”
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DSNET: A Lightweight Segmentation Model for Segmentation of Skin Cancer Lesion Regions by Yucong Chen, Guang Yang, Xiaohua Dong, Junying Zeng, Chuanbo Qin

“…This model achieves optimal segmentation performance while maintaining low model parameters and computational complexity. To reduce the model size and guarantee model segmentation performance, we proposed a detail-enhanced separable difference convolution as a base module in the model. …”
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Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8 by Runyi Lv, Jianping Hu, Tengfei Zhang, Xinxin Chen, Wei Liu

“…First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. …”
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. …”
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Lightweight Multi-Head MambaOut with CosTaylorFormer for Hyperspectral Image Classification by Yi Liu, Yanjun Zhang, Jianhong Zhang

“…While transformers have been widely adopted for hyperspectral image classification due to their global feature extraction capabilities, their quadratic computational complexity limits their applicability for resource-constrained devices. …”
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Lightweight Infrared Small Target Detection Method Based on Linear Transformer by Bingshu Wang, Yifan Wang, Qianchen Mao, Jingzhuo Cao, Han Zhang, Laixian Zhang

“…The model consists of two parts: a multi-scale linear transformer and a lightweight dual feature pyramid network. It combines the strengths of a lightweight feature extraction module and the multi-head attention mechanism, effectively representing the small targets in the complex background at an economical computational cost. …”
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Potato precision planter metering system based on improved YOLOv5n-ByteTrack by Cisen Xiao, Changlin Song, Junmin Li, Min Liao, Yongfan Pu, Kun Du

“…Initially, the C3-Faster module is introduced, which reduces the number of parameters and computational load while maintaining detection accuracy. …”
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A Novel Dangerous Goods Detection Network Based on Multi-Layer Attention Mechanism in X-Ray Baggage Images by Xu Yang, Ting Lan, Yili Xu

“…Compared with some state-of-the-art methods, our network improves performance by 5–10% while reducing parameters and increasing computational efficiency. …”
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S₂Head: Small-Scale Human Head Detection Algorithm by Improved YOLOv8n Architecture by Yuteng Sui, Xinghua Shan, Linlin Dai, Hui Jing, Bo Li, Jianjun Ma

“…Additionally, a small object detection branch and a reparameterizable BiFPN (Rep-BiFPN) structure are incorporated into the neck to improve the model’s sensitivity to small-scale features. Finally, a lightweight MSBlock is also integrated into the head to reduce computational overhead and parameter count without sacrificing detection accuracy. …”
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Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…Inspired by ARM CPU efficiency optimizations, the model uses depthwise separable convolutions and efficient activation functions to reduce parameter size. The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. …”
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Breast mass lesion area detection method based on an improved YOLOv8 model by Yihua Lan, Yingjie Lv, Jiashu Xu, Yingqi Zhang, Yanhong Zhang

“…The YOLOv8-P2 algorithm significantly reduces the number of necessary parameters by streamlining the number of channels in the feature map. …”
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WTSM-SiameseNet: A Wood-Texture-Similarity-Matching Method Based on Siamese Networks by Yizhuo Zhang, Guanlei Wu, Shen Shi, Huiling Yu

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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…SPD-Conv was introduced to replace the original convolutional layers to retain fine-grained information and reduce model parameters and computational costs, thereby improving the accuracy of disease detection. …”
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ILN-YOLOv8: A Lightweight Image Recognition Model for Crimped Wire Connectors by Xiaojian Zhou, Jicheng Kan, Nur Fatin Liyana Mohd Rosely, Xu Duan, Jiajing Cai, Zihan Zhou

“…Taking the original YOLOv8 model as a baseline, the new model enhances the ability to extract shallow features from small targets by increasing the P2 detection layer and improving the Feature Pyramid Network(FPN) and Path Aggregation Network(PAN) structures. …”
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A lightweight semantic segmentation method for concrete bridge surface diseases based on improved DeeplabV3+ by Zhiyuan Yu, Chunquan Dai, Xiaoming Zeng, Yunlong Lv, Haisheng Li

“…Abstract Due to the similar features of different diseases and insufficient semantic information of small area diseases in the surface disease image of concrete bridges, the existing semantic segmentation models for identifying surface diseases in concrete bridges suffer from problems such as large number of parameters, insufficient feature extraction, and low segmentation accuracy. …”
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Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement by Junfeng Wang, Shenghui Huang, Zhanqiang Huo, Shan Zhao, Yingxu Qiao

“…DEP learns overexposure and underexposure corrections simultaneously by employing the ReLU activation function, inverting operation, and residual network, which can improve the robustness of enhancement effects under different exposure conditions while reducing network parameters. Experiments on the LOL-V1 dataset shows BiEnNet significantly increased PSNR by 8.6 $$\%$$ and SSIM by 3.6 $$\%$$ compared to FLW-Net, reduced parameters by 98.78 $$\%$$ , and improved computational speed by 52.64 $$\%$$ compared to the classical KIND.…”
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Full spectrum of novelties in time-dependent urn models by Alessandro Bellina, Giordano De Marzo, Vittorio Loreto

“…We highlight how the TUMT features a “critical” region where both Heaps' and Zipf's laws coexist, for which we compute the exponents.…”
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Research on the lightweight detection method of rail internal damage based on improved YOLOv8 by Xiaochun Wu, Shuzhan Yu

“…Firstly, the GhostHGNetV2 network is adopted as the feature extraction backbone, which reduces computational costs through structural optimization. …”
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Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture by Jintao Liu, Alfredo Tolón-Becerra, José Fernando Bienvenido-Barcena, Xinting Yang, Kaijie Zhu, Chao Zhou

“…Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. …”
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