The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem. This paper proposes a variational autoencoder with optimizing Gaussian mixture model priors. This method utilizes a Gaussian mixture model to construct prior distribution, and utilizes the Kullback-Leibler (KL) distance between posterior and prior distribution to implement an iterative optimization of the prior distribution based on the data. The greedy algorithm is used to solve the KL distance for defining the approximate variational lower bound solution of the loss function, and for realizing the VAE with optimizing Gaussian mixture model priors. Compared with the standard VAE method, the proposed method obtains state-of-the-art results on MNIST, Omniglot, and Frey Face datasets, which shows that the VAE with optimizing Gaussian mixture model priors can learn a better model.

Sleep apnea-hypopnea syndrome (SAHS) is a disease that seriously affects human sleep. Due to its strong concealment and significant harm, it has gradually attracted people's attention. Therefore, it is necessary to use the SAHS detection model to screen the disease as early as possible. However, most of the current SAHS detection models use convolutional neural network (CNN) architectures such as Resnet, which are ineffective in capturing images' details. Moreover, the Vision Transformer (ViT) model relies heavily on the number of SaO2 signal image data during training. Otherwise, it isn't easy to achieve better performance. In addition, due to the existence of fuzzy and uncertain information in the SaO2 signal image, the detection accuracy of previous models is low. Therefore, in this work, we propose a vision transformer enabled deep fuzzy neural network (ViTDFNN) to detect SAHS in an Internet of Medical Things platform. The ViTDFNN model can analyze the patient's vital signs and sleep status on time and identify potential SAHS conditions. The ViTDFNN model first uses ViT to learn the features of the SaO2 signal image so that global information and long-range dependencies can be learned. Subsequently, the ViTDFNN model uses VGG to extract the features of the SaO2 signal image to learn the local features in the image, which is convenient for distinguishing the texture, edge, and other details. Finally, the ViTDFNN model linearly fuses the two image features and inputs them into the deep fuzzy neural network and fully connected network to detect the patient's symptoms. The deep fuzzy neural network can convert each deterministic pixel into an uncertain pixel, thereby solving the fuzzy and uncertain information in the SaO2 signal image. In addition, the ViTDFNN model also uses the masked strategy to form a task of image reconstruction, which is used to reduce the dependence of the ViT model on the labeled SaO2 signal image. We verified the superiority of ViTDFNN by conducting extensive experiments on a real sleep dataset. The scores of the ViTDFNN model under the F1-Score, Recall and Accuracy metrics are 0.994, 0.992 and 0.994, respectively.

A novel regularised image super‐resolution algorithm is proposed, building on the emerging cosparse or analysis sparse prior models, which are important complementary alternatives to the widely used synthesis sparse counterpart. Moreover, to achieve adaptivity to the varying local structures of natural images, the patch space is partitioned into meaningful subspaces by clustering and learn analysis sub‐dictionary for each cluster are partitioned, which are performed online and iteratively based solely on the current available image information, for maximum generality and flexibility. In addition, non‐local feature self‐similarity is incorporated for further reconstruction quality enhancement. Experimental results show that the proposed approach gives favourable results with respect to the state‐of‐the‐art methods.

Monitoring mosquito density to predict the risk of transmission of the virus and develop a response in advance is an important part of prevention efforts. This paper aims to estimate accurately the mosquito swarm count from a given image. To this end, we proposed an attention-based multi-scale mosquito swarm counting model that consists of the feature extraction network (FEN) and attention based multi-scale regression network (AMRN). The FEN uses VGG-16 network to extract low-level features of mosquitoes. The AMRN adopts a multi-scale convolutional neural network, and with a squeeze and excitation channel attention module in the branch with a 7 × 7 convolution kernel to extract high-level features, map the feature map to the mosquito swarm density map and estimate mosquitoes count. We collected and labelled a data set that includes 391 mosquito swarm images with 15,466 mosquitoes. Experiments show that our method performs well on the data set and achieves mean absolute error (MAE) of 1.810 and root mean square error (RMSE) of 3.467.