Accurate and efficient foliage penetration (FOPEN) target recognition plays a vital role in many mission-critical applications, ranging from civilian to surveillance and military. Recently, device-free sensing (DFS), as an emerging technique, has gained great popularity because it requires no dedicated equipment other than wireless transceivers. Although some DFS-based approaches have been successfully applied in foliage environments, they are vulnerable to climate dynamics and heavily rely on re-labeling large amounts of new data when the weather is altered. To address this issue, a CNN-based weather adaptive target recognition network (WATRNet) is proposed in this paper. Specifically, a lightweight weather conditional normalization (WCN) module is embedded atop each convolutional block to encode inputs under different weather conditions into a shared latent feature space. Under an end-to-end learning manner, the proposed WATRNet first learns knowledge from sufficient labeled data under a certain weather condition to achieve a precise classifier. When applying this model under another weather condition, only the WCN module needs to be retrained using limited new labeled samples to learn weather-invariant features, while the rest convolutional parameters in WATRNet are frozen. Consequently, the domain discrepancy caused by climate variations can be adaptively mitigated with as few relabeled data as possible. Comprehensive evaluations are carried out on a real FOPEN dataset collected under four different weather conditions. Experimental results verify that the presented method can achieve over 90% accuracy, even when it implements from a normal weather condition to another severe weather condition with only small amounts of training samples.

The task of infrared-visible person re-identification (IV-reID) is to recognize people across two modalities ( i.e. , RGB and IR). Existing cutting-edge approaches normally use a pair of images that have the same IDs ( i.e. , ID-tied cross-modality image pairs) and input them into an ImageNet-trained ResNet50. The ResNet50 backbone model can learn shared features across modalities to tolerate modality discrepancies between RGB and IR. This work will unveil a Modality Bias Training (MBT) problem that is less discussed in IV-reID, which will demonstrate that MBT significantly compromises the performance of IV-reID. Due to MBT, IR information can be overwhelmed by RGB information during training when the ResNet50 model is pretrained based on a large amount of RGB images from ImageNet. Thus, the trained models are more inclined to RGB information. Accordingly, the cross-modality generalization ability of the model is also compromised. To tackle this issue, we present a Dual-level Learning Strategy (DLS) that 1) enforces the focus of the network on ID-exclusive (rather than ID-tied) labels of cross-modality image pairs to mitigate the problem of MBT and 2) introduces third modality data that contain both RGB and IR information to further prevent the information from the IR modality from being overwhelmed during training. Our third modality images are generated by a generative adversarial network. A dynamic ID-exclusive Smooth (dIDeS) label is proposed for the generated third modality data. In experiments, comprehensive experiments are carried out to demonstrate the success of DLS in tackling the MBT issue exposed in IV-reID.

Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human–Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate.

Achieving energy-efficient massive ultrareliable and low-latency communications (E2-mURLLC) is a promising application prospect for sixth-generation (6G) mobile communication networks. However, there are some insurmountable obstacles, such as a large number of potential users, complex and diverse small-scale and shadow fading, and stringent energy efficiency (EE), reliability, and latency requirements. Considering the above obstacles, we propose a cell-free massive multiple-input–multiple-output (MIMO) architecture based on the $\kappa $ - $\mu $ shadowed fading model, and maximum-ratio combining (MRC) multiuser detection with simple path-loss decoding (S-PLD) to achieve the simultaneous optimization of EE, latency, and reliability. Furthermore, the finite blocklength information theory is used to uncover the relationship among EE, reliability, latency, and achievable data rate when the packet size is small. Simulation results show that compared with the massive MIMO architecture, using our architecture with MRC multiuser detection and S-PLD can support a threefold increase in the number of access users, reduce transmit power by 90%, achieve a nearly 100 times reliability enhancement, and shorten transmission latency by 23.3%. Consequently, a cell-free massive MIMO system with MRC multiuser detection and S-PLD, as a considerable significant potential to facilitate the advancement from URLLC to E2-mURLLC, is promising to support some time-sensitive applications with massive access, such as unmanned aerial vehicles, the Industrial Internet of Things and vehicle-to-vehicle communications.

Recent studies have seen significant advancements in the field of long-term person re-identification (LT-reID) through the use of clothing-irrelevant or insensitive features. This work takes the field a step further by addressing a previously unexplored issue, the Clothing Status Distribution Shift (CSDS). CSDS refers to the differing ratios of samples with clothing changes to those without clothing changes between the training and test sets, leading to a decline in LT-reID performance. We establish a connection between the performance of LT-reID and CSDS, and argue that addressing CSDS can improve LT-reID performance. To that end, we propose a novel framework called Meta Clothing Status Calibration (MCSC), which uses meta-learning to optimize the LT-reID model. Specifically, MCSC simulates CSDS between meta-train and meta-test with meta-optimization objectives, optimizing the LT-reID model and making it robust to CSDS. This framework is designed to prevent overfitting and improve the generalization ability of the LT-reID model in the presence of CSDS. Comprehensive evaluations on seven datasets demonstrate that the proposed MCSC framework effectively handles CSDS and improves current state-of-the-art LT-reID methods on several LT-reID benchmarks.

Device-free sensing (DFS) is an emerging technology that empowers wireless communication systems with the ability for not only data communication but also smart sensing. By taking advantage of machine-learning technologies, DFS transforms traditional wireless communication networks into intelligent context-aware networks and will open the doors for a myriad of promising 6G-enabled Internet of Things (IoT) applications, ranging from smart home to smart buildings. Although significant progress has been made for human activity recognition at a single location by leveraging this technology, performance at multiple locations has not been fully explored. As far as multilocation activity sensing is concerned, the performance is compromised along with the change of locations and labor-intensive annotation works caused by multilocation. To tackle this issue, an activity decomposition network (ActNet) is presented to decompose the activity information directly from input samples by using the training data from different locations together. Instead of dealing with different locations separately, our ActNet can assemble data from different locations together for training to mitigate the data limitation issue caused by a single location. To achieve this, a multiple-input–multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) technology-based prototype system is utilized to collect data samples at 24 different locations in a cluttered office environment. Especially, for each location, only ten samples of each activity are used for training. Experiments demonstrate that the average classification accuracy is 94.6% across all locations with ensured robustness produced by our method.