In recent years, the rise of location-based service applications such as cashier-less shopping, mobile advertisement targeting, and geo-based augmented reality (AR) has been remarkable. These applications offer convenient and interactive experiences by utilizing indoor localization technology. One popular research area in indoor localization is passive fingerprinting localization based on Channel State Information (CSI), which uses general-purpose Wi-Fi platforms and “unconscious cooperative sensing” to achieve device-free localization. However, existing studies face challenges related to inadequate fingerprint richness, limited distinguishability, and inconsistent fingerprint features in real-world dynamic environments. To address these challenges, we prpose MFFLoc in this paper. MFFLoc extracts and processes amplitude and phase information from CSI in a 2D manner. It then fuses the amplitude and phase information using multimodal fusion representation, resulting in rich and distinguishable fused fingerprint features. This approach allows MFFLoc to achieve satisfactory accuracy with just one communication link, reducing deployment costs. To overcome the issue of inconsistent fingerprint features in dynamic environments, MFFLoc proposes an unsupervised domain adaptation method. It employs a dual-flow structure, with one flow operating in the source domain and the other in the target domain. The adaptation layer, with correlated weights, remains unshared between the two flows. Meta-learning is also used to automatically determine the most suitable adaptation layer. Through extensive 6-day experiments conducted in a dynamic indoor environment, MFFLoc showcases superior performance compared to state-of-the-art systems. It demonstrates higher localization accuracy and robustness, making it a promising solution for indoor localization applications.

With the rapid development of location-based IoT applications in recent years, indoor device-free passive localization based on Wi-Fi channel state information (CSI) has attracted considerable attention. In this article, we propose a long-term effective, robust, and accurate device-free passive fingerprinting localization scheme LTLoc, which only requires a single communication link. It takes the amplitudes extracted from the CSI along with the calibrated phases as fingerprints and trains a deep neural network (DNN) regression model to estimate the target location. Since Wi-Fi signals are susceptible to various environmental factors, CSI fingerprints also change over time, making the performance of the localization model built with the fingerprint database drop dramatically over a long period, and recalibrating the entire positioning area is laborious and time-consuming. To address this problem, we design an adaptive DNN (AdaptDNN) based on meta-networks by combining deep learning and domain adaptive methods. It can use meta-network learning to determine which layers and features of the DNN need to be transferred to automatically adapt to CSI fingerprints change. Extensive evaluations in an indoor environment with significantly different CSI fingerprints over six days have shown that LTLoc’s effectiveness in coping with changing CSI fingerprints over a long period is significantly superior to existing work in terms of localization and adaptability.

In recent years, indoor localization has attracted a lot of interest and has become one of the key topics of Internet of Things (IoT) research, presenting a wide range of application scenarios. With the advantages of ubiquitous universal Wi-Fi platforms and the "unconscious collaborative sensing" in the monitored target, Channel State Information (CSI)-based device-free passive indoor fingerprinting localization has become a popular research topic. However, most existing studies have encountered the difficult issues of high deployment labor costs and degradation of localization accuracy due to fingerprint variations in real-world dynamic environments. In this paper, we propose BSWCLoc, a device-free passive fingerprint localization scheme based on the beyond-sharing-weights approach. BSWCLoc uses the calibrated CSI phases, which are more sensitive to the target location, as localization features and performs feature processing from a two-dimensional perspective to ultimately obtain rich fingerprint information. This allows BSWLoc to achieve satisfactory accuracy with only one communication link, significantly reducing deployment consumption. In addition, a beyond-sharing-weights (BSW) method for domain adaptation is developed in BSWCLoc to address the problem of changing CSI in dynamic environments, which results in reduced localization performance. The BSW method proposes a dual-flow structure, where one flow runs in the source domain and the other in the target domain, with correlated but not shared weights in the adaptation layer. BSWCLoc greatly exceeds the state-of-the-art in terms of positioning accuracy and robustness, according to an extensive study in the dynamic indoor environment over 6 days.

Deep-learning-based multiple label chest X-ray classification has achieved significant success, but existing models still have three main issues: fixed-scale convolutions fail to capture both large and small lesions, standard pooling is lacking in the lack of attention to important regions, and linear classification lacks the capacity to model complex dependency between features. To circumvent these obstacles, we propose CONVFCMAE, a lightweight yet powerful framework that is built on a backbone that is partially frozen (77.08 % of the initial layers are fixed) in order to preserve complex, multi-scale features while decreasing the number of trainable parameters. Our architecture adds (1) an intelligent global pooling module that is learnable, with $1\times 1$ convolutions that are dynamically weighted by their spatial location, and (2) a multi-head attention block that is dedicated to channel re-calibration, along with (3) a two-layer MLP that has been enhanced with ReLU, batch normalization, and dropout. This module is used to enhance the non-linearity of the feature space. To further reduce the noise associated with labels and the imbalance in class distribution inherent to the NIH ChestXray14 dataset, we utilize a combined loss that combines BCEWithLogits and Focal Loss as well as extensive data augmentation. On ChestXray14, the average ROC–AUC of CONVFCMAE is 0.852, which is 3.97 percent greater than the state of the art. Ablation experiments demonstrate the individual and collective effectiveness of each component. Grad-CAM visualizations have a superior capacity to localize the pathological regions, and this increases the interpretability of the model. Overall, CONVFCMAE provides a practical, generalizable solution to the problem of extracting features from medical images in a practical manner.