Medical images are susceptible to noise and artifacts, so denoising becomes an essential pre-processing technique for further medical image processing stages. We propose a medical image denoising method based on dual-attention mechanism for generative adversarial networks (GANs). The method is based on a GAN model with fused residual structure and introduces a global skip-layer connection structure to balance the learning ability of the shallow and deep networks. The generative network uses a residual module containing channel and spatial attention for efficient extraction of CT image features. The mean square error loss and perceptual loss are introduced to construct a composite loss function to optimize the model loss function, which helps to improve the image generation effect of the model. Experimental results on the LUNA dataset and “the 2016 Low-Dose CT Grand Challenge” dataset show that DAGAN achieves the best results in root mean square error (RMSE), structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) when compared to the state-of-the-art methods. In particular, PSNR reaches 31.2308 dB and 27.5265 dB, SSIM reaches 0.9115 and 0.7895, while RMSE is 0.0082 and 0.0112, respectively. This indicates that our method performs better than the state-of-the-art methods in the task of CT image denoising.

Unsupervised anomaly detection methods based on knowledge distillation have exhibited promising results. However, there is still room for improvement in the differential characterization of anomalous samples. In this paper, a novel anomaly detection and localization model based on reverse knowledge distillation is proposed, where an enhanced multi-scale feature mutual mapping feature fusion module is proposed to greatly extract discrepant features at different scales. This module helps enhance the difference in anomaly region representation in the teacher-student structure by inhomogeneously fusing features at different levels. Then, the coordinate attention mechanism is introduced in the reverse distillation structure to pay special attention to dominant issues, facilitating nice direction guidance and position encoding. Furthermore, an innovative single-category embedding memory bank, inspired by human memory mechanisms, is developed to normalize single-category embedding to encourage high-quality model reconstruction. Finally, in several categories of the well-known MVTec dataset, our model achieves better results than state-of-the-art models in terms of AUROC and PRO, with an overall average of 98.1%, 98.3%, and 95.0% for detection AUROC scores, localization AUROC scores, and localization PRO scores, respectively, across 15 categories. Extensive experiments are conducted on the ablation study to validate the contribution of each component of the model.

Indoor gesture recognition technology is concerned with making the machine accurately recognize dynamic gestures within a certain range. Remarkably, most of this technology is based on passive recognition methods. This is quite striking because the high cost is a crucial factor in active recognition methods and ignoring this aspect can increase the reality gap. In this paper, we tend to use the fine-grained channel state information (CSI) in Wi-Fi to build a dynamic CNN-GRU-Attention (CGA) model to implement a gesture recognition system and thus alleviate this problem. Firstly, we study the influence of gestures on the amplitude and phase difference in CSI, and prove the feasibility of proposed method by analyzing the fluctuation of amplitude and phase difference under different conditions. Then, we use data processing methods such as phase correction and unwrapping with a new proposed adaptive gesture action truncation algorithm to extract the phase difference and remove redundant information, thus ensuring the validity of data. Finally, we propose to segment gesture fragment into 3-channel CSI images as input information of model. Extensive comparison experiments are conducted under the influence of different people, different indoor environments , and different sampling rates . The results show that the system has high accuracy.

As an important positioning source of indoor positioning technology, Wi‐Fi signals have attracted the attention of researchers for a long time. Fingerprint positioning can solve the problems caused by non‐line‐of‐sight propagation and multipath effects. To improve the accuracy of Wi‐Fi indoor positioning, this study proposes an indoor positioning algorithm based on fine‐grained channel state information (CSI) and convolutional neural network (CNN). CSI is a kind of observable measurement that better describes the nature of Wi‐Fi signal propagation than received signal strength indication. This method uses the subcarrier amplitude and phase difference information extracted from CSI data to establish fingerprints. The clustering method is used to analyse the number of clusters of fingerprint data, and the fingerprint database is divided into two sub‐databases according to the threshold. CNNs with the same network structure are used to train the two kinds of fingerprint sub‐databases. In the positioning stage, the sub‐database to which the data to be measured belongs is determined according to the calibration algorithm, and the corresponding CNN model is used to estimate the position. Experiments are performed in a typical indoor environment. Compared with existing fingerprint‐based positioning methods, this method has higher positioning accuracy.