Chaos-based communication technology has emerged as a research hotspot in recent years due to its superior resistance to multipath fading and robust security features. Differential Chaos Shift Keying (DCSK), as a non-coherent digital modulation scheme, has attracted widespread attention. However, in practical communication scenarios, the increasing demand for reliable data transmission has revealed the limitations of traditional DCSK systems, such as low transmission rates and high Bit Error Ratios (BER), highlighting the urgent need to enhance system reliability. Considering the significant advantages of polar codes, including low complexity and near-capacity performance, this paper delves into the integration of polar coding algorithms with chaos modulation techniques based on channel polarization principles, aiming to further improve the reliability of chaos-based communication systems. Experiment results show that the proposed solution significantly improves the reliability of chaos-based communication systems and keeps its feature of low complexity.

Driven by the ever-increasing supply of computational resources, the parameter size of Large Language Models(LLMs) continues to expand and their task performance in natural language processing has become more superior. However, there are still limitations when faced with reasoning problems, especially in common-sense reasoning or mathematical problems. Chain of Thought(CoT) significantly improves its ability to solve problems in different domains by guiding the model to generate reasoning steps. In this paper, we not only sort out the theoretical foundation system and technical evolution of CoT from the perspective of training method, but also further discuss application scenarios such as government service and enterprise digitalisation. Finally, in the light of the development trend of Artificial Intelligence (AI), the paper discusses the essential role of CoT in the development of LLMs towards a higher cognitive level from the perspective of the degree of AI, and points out the challenges and technical bottlenecks that need to be solved at the present time.

Reconfigurable Intelligent Surface (RIS) and Rate Splitting Multiple Access (RSMA) technologies are two emerging communication techniques with broad prospects in future wireless systems. Active RIS (ARIS) has the advantage of overcoming the effects of multiplicative fading compared to Passive RIS (PRIS). The paper addresses the outage performance of ARIS-assisted RSMA systems under random deployment. Under Nakagami-m small-scale fading model in downlink, multiple users are randomly distributed within a semicircular region around ARIS and sorted by the distances between the users and ARIS. Taking the optimal phase shift for the furthest user as the entry point, a moment matching approach is employed to derive the shape and scale parameters associated with the cascaded channel power characteristic for each RSMA user. We derive a closed-form expression for the outage performance of the users, analyze the effect of the power splitting coefficients on the Outage Probability (OP), and obtain the diversity order and an expression for the approximate OP. Simulation results show that the OP of a non-perfect phase-shifted user under ARIS-assistance is re duced by 58% compared to the passive RIS-assisted system under a power budget of 50 dBm and a moderate amount of RIS elements. In addition, the power consumption of the user with the optimal phase shift is reduced by 53 dBm.

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.

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.

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 future 6G Internet of Things (IoT) systems, extensive deployment of pivotal technologies such as high-frequency millimeter waves and terahertz spectrum makes it possible for wireless transmission among network devices situated in the near-field region. As a byproduct, Dynamic Metasurface Antenna (DMA) and other small-sized antenna arrays have been widely applied in this scenario due to their advantages in transmission efficiency, physical size, and power consumption. And related research has received increasing attention. Aiming to improve the energy performance of receivers in near-field wireless transmission, a downlink near-field wireless Simultaneous Wireless Information and Power Transfer (SWIPT) system based on DMA is proposed. Under the condition of satisfying the minimum transmission rate requirements of all information users, an efficient solution for jointly optimizing the tunable frequency response matrix of DMA and the digital precoding vector is proposed for this optimization problem. In addition, the influences of factors such as the distance between users and the minimum Signal to Interference plus Noise Ratio (SINR) on the system performance are also discussed on this basis. Simulation results show that the scheme proposed in this paper can effectively improve the joint performance of wireless information and power transmission compared with other existing technologies.

In this paper, a Wireless Powered Communication Network (WPCN) based on Active Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (ASTAR-RIS) is proposed. The communication network is mainly composed of four parts: Power Station (PS), Sensor Node Groups (SNGs), ASTAR-RIS and Access Point (AP). The operation process of the communication system is mainly divided into two stages: Wireless Energy Transfer (WET) stage and Wireless Information Transfer (WIT) stage. Energy Splitting (ES) mode is adopted in the wireless energy transmission phase, and Time Switching (TS) mode is adopted in the wireless information transmission phase. This paper aims to optimize the phase shift parameters and communication resource allocation of ASTAR-RIS to maximize system throughput. Since the optimization problem is non-convex, this paper uses an alternate optimization algorithm to solve the problem. Firstly, the problem is divided into two parts according to the coupled variables. The optimal solutions of the variables in these two parts are solved by Semidefinite Relaxation (SDR) and Fractional Programming (FP) respectively. Experimental results show that the communication scheme proposed in this paper can provide higher performance gain for the system.

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.

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%.