Lunar crater detection plays a crucial role in geological analysis and the advancement of lunar exploration. Accurate identification of craters is also essential for constructing high-resolution topographic maps and supporting mission planning in future lunar exploration efforts. However, lunar craters often suffer from insufficient feature representation due to their small size and blurred boundaries. In addition, the visual similarity between craters and surrounding terrain further exacerbates background confusion. These challenges significantly hinder detection performance in remote sensing imagery and underscore the necessity of enhancing both local feature representation and global semantic reasoning. In this paper, we propose a novel Spatial Channel Fusion and Context-Aware YOLO (SCCA-YOLO) model built upon the YOLO11 framework. Specifically, the Context-Aware Module (CAM) employs a multi-branch dilated convolutional structure to enhance feature richness and expand the local receptive field, thereby strengthening the feature extraction capability. The Joint Spatial and Channel Fusion Module (SCFM) is utilized to fuse spatial and channel information to model the global relationships between craters and the background, effectively suppressing background noise and reinforcing feature discrimination. In addition, the improved Channel Attention Concatenation (CAC) strategy adaptively learns channel-wise importance weights during feature concatenation, further optimizing multi-scale semantic feature fusion and enhancing the model’s sensitivity to critical crater features. The proposed method is validated on a self-constructed Chang’e 6 dataset, covering the landing site and its surrounding areas. Experimental results demonstrate that our model achieves an $mA{P}_{0.5}$ of 96.5% and an $mA{P}_{0.5:0.95}$ of 81.5%, outperforming other mainstream detection models including the YOLO family of algorithms. These findings highlight the potential of SCCA-YOLO for high-precision lunar crater detection and provide valuable insights into future lunar surface analysis.

Ultra-wideband (UWB) indoor positioning relies on precise base station (BS) locations. However, regular receiver positioning may become unfeasible in non-cooperative environments or when BS locations are inaccurate. Existing BS establishment methods require manual calibration using techniques such as laser ranging or automated surveying with auxiliary information sources like IMU. Efficient estimation and calibration of base station positions solely based on relative ranging measurements remains unattainable. To address the issue, this study proposes a BS position self-calibration method that solely relies on relative ranging measurements, solving the problem of the automatic estimation of non-cooperative BS positions in complex indoor environments. Firstly, a sliding-window least squares (LS) algorithm is proposed to achieve the preliminary estimation of the BS positions and the receiver positions simultaneously while constraining the indoor propagation path errors. Following this, a clustering weighting optimization algorithm is proposed to mitigate multipath effects and converge the preliminary results to final BS locations. Experimental results show that under the 3D indoor positioning condition, the maximum error of the base station position estimation is less than 0.5m, fully meeting the decimeter-level indoor positioning requirements for Internet of Things (IoTs) applications.

Electrocardiogram (ECG) signals contain information about both user identity and physical activity, but individual physiological differences complicate activity inference. We propose DT4ECG, a subject-aware multi-task framework for joint identity recognition (IDR) and human activity recognition (HAR) among enrolled users. A Multi-Scale Residual Convolution (MSRC) backbone extracts complementary waveform and temporal features, while a Subject Adaptive Fusion Module (SAFM) uses learned identity-related representations to calibrate activity features. Evaluation on two public datasets and one self-built dataset achieves IDR macro F1 of 98.59--99.96\% and HAR macro F1 of 95.40--96.82\%. Ablation results show consistent HAR improvements from SAFM across the three datasets, and recorded-noise tests characterize sensitivity to common ECG artifacts. The two-channel model contains 0.361 million parameters. These findings demonstrate the value of treating subject-related physiology as context for activity recognition. By linking activity estimates to user attribution within a single ECG framework, this work provides a basis for integrated physiological monitoring among enrolled users in shared-device settings.

This study introduces an improved VMD based signal decomposition methodology for non-contact heartbeat estimation using millimeterwave (mmWave) radar. Specifically, we first analyze the signal model of the mmWave radar system. The Variational Mode Decomposition (VMD) integrated with the Newton-Raphson-based optimizer (NRBO) algorithm are sequentially utilized for cardiac mechanic signal (CMS) reconstruction. The estimation accuracy is enhanced by adaptively optimizing the VMD parameters including intrinsic mode functions (IMFs) and penalty factor. Eventually, the experimental results of 18 subjects validate the effectiveness of the proposed method by comparing with three commonly used baselines.