In smart grids, the presence of numerous high-power electrical devices and communication sensing equipment severely hinders signal transmission in positioning systems. To address the challenge of accurately locating weak signals in a Reconfigurable Intelligent Surface (RIS)-assisted Non-Orthogonal Multiple Access (NOMA) system under interference from multiple base stations and communication users, this paper considers the impact of multiple small base stations and multiple users in a smart grid environment. A horizontal positioning error of the target user is used as the evaluation metric. While ensuring the Quality of Service (QoS) for communication users, the proposed method jointly optimizes base station power, multi-user interference, and power allocation factors. The Lagrangian dual method and sub-gradient approach are employed to solve the non-convex optimization problem caused by multiple users and small base stations. Simulation results demonstrate that, under the same resource allocation, the proposed RISNOMA integrated sensing and communication system significantly reduces the average positioning error compared to traditional NOMA-based system.

With the vigorous development of Internet of Things technology, a large number of terminal devices have been widely deployed. As a result, the challenging issues of energy replenishment for massive terminal devices and the congestion of the frequency spectrum have become increasingly prominent. These not only limit the further development of the Internet of Things but also pose a severe challenge to existing network infrastructure. Low-power Internet of Things, as a key technology to address these issues, has received extensive attention from researchers. As a result, a survey on low-power Internet of Things is studied in this paper. Firstly, an overview of low-power Internet of Things is provided, including its principles and various low-power communication technologies. Secondly, based on existing research achievements, main transmission architectures of low-power Internet of Things are analyzed. Subsequently, aiming at the complex communication environment in the Internet of Things, communication architectures of low-power Internet of Things under different propagation environments are presented. Then, typical application scenarios of existing low-power In ternet of Things are discussed, demonstrating its potential value in multiple fields. Finally, future research trends of low-power Internet of Things are prospected and outlined.

Skin cancer and melanocytic nevus share numerous similarities, which can result in a misdiagnosis by dermatologists. To improve the screening accuracy of early skin cancer patients, the Gamma Transform Block (GMTB) based on Gamma Transform (GT) and Wavelet Convolution Block (WTCB) based on Wavelet Transform (WT) are proposed. Furthermore, the Space-Frequency Transform Network (SFTNet) for capturing fine-grained features of skin cancer is innovatively proposed based on the Detection Transformer(DETR) architecture. SFTNet-based skin cancer screening system can effectively improve disease detection accuracy because it enhances the sample image at different channels and reduces over-fitting effect during the model training process. Simulation results on HAM10000 dataset show that the accuracy of this system can reach 85.5%, which underscores the significant clinical value of our approach in skin cancer assisted diagnosis.

Seepage monitoring is crucial for the safe operation and maintenance of dams. Traditional dam observation methods suffer from significant random errors and insufficient inspection frequency during flood seasons. To address these limitations, this study proposes an infrared thermography-based unmanned aerial vehicle inspection system for detecting surface seepage on dam bodies. First, an image dataset of seepage-affected areas on the dam surface was collected and established using an infrared camera. Then, an improved Mask Region-based Convolutional Neural Network(Mask R-CNN) framework was employed to extract seepage region data, enabling rapid detection of surface seepage. Subsequently, binary processing was applied to quantify the seepage area. Finally, the proposed method was validated on the downstream face of a hydropower station. Experimental results demonstrate that the proposed approach reduces the inspection cycle by 80% compared to traditional methods while maintaining sufficient accuracy for routine dam monitoring. This study provides a novel technique for seepage detection and quantitative analysis, offering a new solution for dam leakage inspection and seepage-related damage assessment.

In recent years, Transformer-based visual models (e. g. , Swin Transformer) show good prospects in visual tasks, however, these methods usually focus on reducing signal distortion between original and reconstructed data, while ignoring perceptual quality. Considering that the conventional Mean Square Error (MSE) loss fails to reflect perceptual and semantic quality effectively, we propose a weighted loss function combining MSE and Learned Perceptual Image Patch Similarity (LPIPS), and accordingly construct a Swin Transformer-based semantic communication framework, called Swin Transformer with LPIPS-based Joint Source-Channel Coding (STL-JSCC) method, which significantly enhances image reconstruction quality and semantic consistency. For performance evaluation, two semantic-aware metrics are introduced: the Images Semantic Deviation (ISD) value and Iamges Semantic Similarity(ISS). These indicators form a joint perceptual-semantic evaluation system, which breaks through the limitations of traditional evaluation methods. Experimental results show that the proposed STL-JSCC outperforms other models in all the indexes, verifying the significant potential and advantages of the proposed method in improving the image reconstruction quality and semantic extraction capability.

Existing key point detection algorithms tend to suffer from reduced detection precision, missed detections, or misaligned key points in scenarios with varying lighting conditions and dense crowds with overlapping figures. To address this issue, an improved LBW-YOLOv8n-Pose algorithm for multi-person pose estimation in complex environments is proposed based on YOLOv8n-Pose. By introducing the Large Separable Kernel Attention (LSKA) in the Spatial Pyramid Pooling-Fast (SPPF) layer of the feature extraction backbone network, the algorithm enhances the image feature representation and perception capabilities. A weighted Bidirectional Feature Pyramid Network (BiFPN) is incorporated in the neck network for reconstruction to improve the multi-scale feature fusion effect. Additionally, an improved Wise-IoU loss function is adopted to accelerate the model's convergence speed and enhance its robustness in complex scenarios. Experimental results show that the improved model achieves precision, recall, and average detection precision of 85.7%, 76.8%, and 81.7% respectively on the MS-COCO2017 human key point dataset, representing significant improvements over the original model. Moreover, it can more accurately and effectively detect key point information of multiple people in complex situations.

Compared with a different-color backgrounds, recognizing and detecting cucumber fruits under uniform-color backgrounds remains a key challenge due to limited distinguishing features and increased susceptibility to occlusion and background interference. To address this, we propose YOLO-ACG, a detection network based on YOLOv11n. An Adaptive Dynamic Downsample (A-Down) module is introduced, combining deformable convolution and channel attention to achieve adaptive cross-scale feature sampling. A Ghost_HGNetV2 architecture is designed, where the High-resolution Group Stem (HGStem) reduces input channels to extract efficient intrinsic features, and the Ghost_HGBlock applies knowledge distillation to enhance feature representation. A Context and Spatial Feature Calibration Network (CSFCN) network structure is introduced, which includes Context Feature Calibration (CFC) and Spatial Feature Calibration (SFC). The CFC module aggregates context information relevant to each pixel, while the SFC module leverages calibrated spatial features to ensure accurate understanding of spatial layout the image. Together, they enable the network to more precisely distinguish cucumber fruits from backgrounds with similar colors. Experimental results show that the improved model achieves 4.64 percentage points increase in precision, recall by 5.07 percentage points, F1 by 4.89 percentage points, and mAP by 4.48 percentage points. Ablation and comparative experiments confirm that YOLO-ACG significantly reduces false positives and missed detections, offering effective technical support for cucumber fruits recognition in complex, uniform-color environments.

To address the challenges of data scarcity, stylistic diversity, and complex textures in art image classification, a novel self-supervised learning framework is proposed——Frequency-Masked Contrast (F-MaCo). Built upon a dual-branch contrastive learning paradigm, F-MaCo leverages a two-dimensional Discrete Wavelet Transform (DWT) to project images into the frequency domain, enabling dynamic frequency-domain masking augmentation. Additionally, a perceptual loss-driven weighting mechanism is introduced to effectively capture the multi-scale features and rich textures information of art images. Experimental results demonstrate that F-MaCo achieves state-of-the-art performance on four art image datasets—MAMe, Kaokore, Artbench10, and ArtDL—with Top1 accuracies of 73.72%, 77.38%, 58.38%, and 68.31%, respectively, validating its effectiveness and robustness in art image representation learning.

Focusing on the channel estimation accuracy degradation caused by the beam splitting effect in near-field wideband Extremely-Large Scale Multiple Input Multiple Output (XL-MIMO) systems, this paper proposes a Bi-Directional Integrated Multi-Subcarrier Augmented Bilinear Pattern Detection (BDI-MSABPD) algorithm. Built upon the polar-domain sparse representation framework, the proposed method addresses both the sparse support set misalignment and parameter estimation bias induced by beam splitting through a dual mechanism combining explicit polar-domain resolution enhancement and implicit multi-subcarrier joint optimization. Simulation results demonstrate that the BDI-MSABPD achieves an average reduction of 2 dB in Normalized Mean Squared Error (NMSE) compared with conventional Bilinear Pattern Detection (BPD) algorithm.