2023
DOI: 10.1049/cmu2.12628
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An effective data communication community establishment scheme in opportunistic networks

Abstract: The network transmission speed has been greatly improved, thanks to the power of 5G technology. The millisecond‐level communication delay has made a qualitative leap in communication quality. However, the sharp increase in the number of nodes connected to the internet has resulted in an explosion of traffic. Ensuring stable network transmission in the face of large data volumes has become an urgent problem to be solved. Existing research mainly optimizes for low data volumes of nodes, and cannot dynamically ad… Show more

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Cited by 6 publications
(2 citation statements)
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References 69 publications
(68 reference statements)
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“…Putting the entire pathological image directly into the CNN-based network training will lead to poor performance of the model [47]. This is due to the loss of discriminative details due to the need for extensive image downsampling, and the network only learns from one of many discriminative patterns [48,49]. To avoid these problems, we need to binarize original osteosarcoma pathology images using the OSTU algorithm and crop the binarized images.…”
Section: Location Of Roimentioning
confidence: 99%
“…Putting the entire pathological image directly into the CNN-based network training will lead to poor performance of the model [47]. This is due to the loss of discriminative details due to the need for extensive image downsampling, and the network only learns from one of many discriminative patterns [48,49]. To avoid these problems, we need to binarize original osteosarcoma pathology images using the OSTU algorithm and crop the binarized images.…”
Section: Location Of Roimentioning
confidence: 99%
“…Nonetheless, in spite of the commendable performance of convolutional neural networks in semantic segmentation of pathological slides, they still have limitations in capturing shape and structural information and lack efficiency. Furthermore, there are significant segmentation differences in size and shape among different pathological slide images [50]. To address the limitations of convolutions, the Visual Transformer (ViT) [51] was proposed, which relies solely on the multi-head self-attention mechanism.…”
Section: Related Workmentioning
confidence: 99%