Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.

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The existence of humans and the preservation of the natural ecological equilibrium depend greatly on trees. The semantic segmentation of trees is very important. It is crucial to learn how to properly and automatically extract a tree’s elements from photographic images. Problems with traditional tree image segmentation include low accuracy, a sluggish learning rate, and a large amount of manual intervention. This research suggests the use of a well-known network segmentation technique based on deep learning called Yolo v7 to successfully accomplish the accurate segmentation of tree images. Due to class imbalance in the dataset, we use the weighted loss function and apply various types of weights to each class to enhance the segmentation of the trees. Additionally, we use an attention method to efficiently gather feature data while reducing the production of irrelevant feature data. According to the experimental findings, the revised model algorithm’s evaluation index outperforms other widely used semantic segmentation techniques. In addition, the detection speed of the Yolo v7 model is much faster than other algorithms and performs well in tree segmentation in a variety of environments, demonstrating the effectiveness of this method in improving the segmentation performance of the model for trees in complex environments and providing a more effective solution to the tree segmentation issue.

With the increasing demand of location-based services, channel state information (CSI) has attracted great interest because of the fine-grained information it provides. In this paper, we propose an original network structure, which exploits both the local information and global information in CSI amplitude for fingerprint localization. First, we validate the correlation between adjacent subcarriers and introduce the position-dependent local feature (PDL-feature). Next, local connection based deep neural network (LC-DNN) is designed to improve positioning performance by extracting and exploiting the correlation between adjacent subcarriers for indoor localization. LC-DNN consists of locally-connected layer and fully-connected layer. In the locally-connected layer, the variation of CSI amplitude in local frequency range is extracted and spliced for rich information. The frequency range and the times of extraction are determined by receptive field length and step size respectively. In the fully-connected layer, not only global features of CSI amplitude are further extracted, but also the function between features and position coordinates is obtained. Experiments are conducted to validate the effectiveness of LC-DNN and investigate the influence of hyper parameters on localization. Moreover, the positioning performance of LC-DNN is compared with four methods based on deep neural networks (DNNs). Results show that LC-DNN performs well in positioning accuracy and stability, with the mean error of 0.78m. INDEX TERMS Indoor localization, deep neural network (DNN), position-dependent local feature (PDLfeature), local connection, channel state information (CSI)