Predicting trajectories of key individuals plays an important role in preventing potential criminal activities, optimizing emergency response, and intelligence analysis. Application of this technology by public security departments helps maintain social stability, improve urban management efficiency, and improve economic development. However, existing techniques face challenges in adapting to dynamic environments, neglecting the scope of social influence, and influence quantification of neighborhood moving objects. A novel model for predicting long-term trajectory areas of key individuals based on destination-intention learning by integrating spatio-temporal queries, is proposed. Firstly, aiming to solve the problem of capturing the spatio-temporal features of moving object trajectories, a key individuals trajectory prediction model called Spatio-Temporal Multiple Attention (STMA) is introduced. It can enhance the model sensitivity to the change of behavioral features by capturing temporal dependencies and spatial interactions through temporal and spatial attention modules, respectively. Secondly, in order to cope with the problem of quantifying the social influence, a social force function is constructed to simulate the social influence of pedestrians. The virtual contour construction method and the social force function can accurately simulate dynamic behaviors and improve the efficiency of influence capture. Experiments based on real-world traffic datasets show that, compared to the state-of-the-art trajectory prediction algorithms, STMA demonstrates higher accuracy and reliability in long-term and short-term trajectory prediction. In terms of long-term forecasting, the STMA model achieves an average accuracy rate of 54.3%, outperforming Sophie by 29.3%, Social Spatio Temporal Graph Convolutional Neural Network (S-STGCNN) by 13.4%, Conditional Generative Neural System (CGNS) by 36.8%.

Based on the principle of Orthogonal Time Frequency and Space (OTFS) modulation, this paper designs an OTFS waveform scheme based on Zero Suffix (ZP) protection. Methods of synchronization, channel estimation, and the detection algorithm based on delay-time domain Maximum Ratio Combining (MRC) are presented, and the MRC detection algorithm is simplified. Hardware implementation schemes of channel interpolation and the MRC detector are given. And the Field Programmable Gate Array(FPGA)hardware implementation of the proposed OTFS system waveform is carried out to verify the feasibility of the key algorithms of the designed OTFS system. Test results show that the designed OTFS system has good performance in resisting doubly selective fading.

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.

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.

Multiple Input Multiple Output (MIMO) technology significantly enhances signal transmission rates and system reliability through multi-antenna systems. To improve spectral efficiency and anti-interference capabilities, spatial modulation technology, as an extension of MIMO, has been proposed and widely applied. Generalized Spatial Modulation (GSM) further integrates multiple modulation schemes, enhancing the system's performance. Polar codes, as an efficient error correction code, leverage channel polarization to transform physical channels into virtual channels with varying levels of reliability, thus effectively improving the performance of MIMO and spatial modulation systems. This paper presents a decoding scheme for multi-user polar codes, aimed at optimizing the decoding process in the uplink Polar Coded-Generalized Spatial Modulation (PC-GSM) system. By combining the channel polarization characteristics of polar codes with the advantages of GSM, the scheme improves decoding algorithms, enhancing the reliability and data transmission rate of multi-user systems. Simulation results show that the proposed decoding scheme significantly boosts system performance, providing a novel solution for the integration of multi-user polar codes and spatial modulation technology.

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.

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.

In contemporary society, Global Navigation Satellite System (GNSS) has become an essential tool for daily travel, significantly improving travel efficiency. However, in environments with weak signals, such as indoors or tunnels, GNSS systems often experience signal loss due to insufficient signal strength, leading to positioning failure and inability to provide accurate navigation services. To address this challenge, this paper proposes a high-precision positioning solution based on an improved Extended Kalman Filter (EKF). This solution integrates Ultra-Wideband (UWB) least squares method, KF, and EKF technologies, and introduces a Multi-Innovation EKF (MIEKF) algorithm. By utilizing multi-time observation data and a forgetting factor mechanism, the solution effectively reduces positioning errors and enhances positioning accuracy. Experimental results show that the root mean square error of this solution can be reduced to 0.179 m, verifying its high-precision positioning capability in weak signal environments and providing reliable technical support for precise navigation in complex scenarios.

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.