WiFi localization based on channel state information (CSI) fingerprints has become the mainstream method for indoor positioning due to the widespread deployment of WiFi networks, in which fingerprint database building is critical. However, issues, such as insufficient samples or missing data in the collection fingerprint database, result in unbalanced training data for the localization system during the construction of the CSI fingerprint database. To address the above issue, we propose a deep learning-based oversampling method, called Self-Attention Synthetic Minority Oversampling Technique (SASMOTE), for complementing the fingerprint database to improve localization accuracy. Specifically, a novel self-attention encoder-decoder is firstly designed to compress the original data dimensionality and extract rich features. The synthetic minority oversampling technique (SMOTE) is adopted to oversample minority class data to achieve data balance. In addition, we also construct the corresponding CSI fingerprinting dataset to train the model. Finally, extensive experiments are performed on different data to verify the performance of the proposed method. The results show that our SASMOTE method can effectively solve the data imbalance problem. Meanwhile, the improved location model, 1D-MobileNet, is tested on the balanced fingerprint database to further verify the excellent performance of our proposed methods.

In recent years, various deep-learning methodologies have been developed for processing medical images, with Unet and its derivatives proving particularly effective in medical image segmentation. Our primary objective is to enhance the accuracy of these networks while also reducing the number of parameters and computational demands to facilitate deployment on mobile medical devices. To this end, we introduce a novel medical image segmentation network, MSLUnet, which aims to minimize parameter count and computational load without compromising segmentation effectiveness. The network features a U-shaped architecture. In the encoder module, we utilize multiple small convolutional kernels for successive convolutions rather than large ones, allowing for capturing multi-scale feature information at granular levels through varied receptive field scales. In the decoder module, an inverse bottleneck structure with depth-separable convolution employing large kernels is incorporated. This design effectively extracts spatial dimensional information and ensures a comprehensive integration of both shallow and deep features. Additionally, a lightweight three-branch attention mechanism within the skip connections enhances information transfer by capturing global contextual data across spatial and channel dimensions. Experimental evaluations conducted on several publicly available medical image datasets indicate that MSLUnet is more competitive than existing models in terms of efficiency and effectiveness.