To address the limitations of single-source localization methods in complex indoor environments, such as insufficient accuracy and stability, this paper proposes an indoor localization method based on multi-modal information fusion of Wi-Fi channel state information (CSI) fingerprint images and ZigBee received signal strength indication (RSSI). First, Hampel filtering is applied to preprocess CSI signals, and both amplitude and phase information of CSI are combined to form high-resolution image fingerprint data. For RSSI signals, data packets collected by ZigBee sensor networks are processed through outlier removal and matrix transformation to generate corresponding fingerprint data. Inspired by image classification tasks, a lightweight efficient channel attention convolutional neural network (ECA-CNN) is designed to extract and train features from CSI fingerprint images, while a transformer network is utilized to train RSSI fingerprint data. Finally, a soft voting method integrates the fingerprint databases from both models to produce classification outputs. Experimental results demonstrate that this method significantly improves localization accuracy and robustness in indoor environments, effectively overcoming the limitations of single-source localization.
In this paper, CdxZn1−xS thin films were deposited on glass substrates by chemical bath deposition (CBD), and the effects of different concentrations of ammonia water on the morphology, structure and optical properties of the films were studied. CdxZn1−xS thin films have hexagonal crystal structure, the transmittance is above 75
This paper considers the physical layer security (PLS) performance analysis of an unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) communication system over Fisher-Snedecor (F) distributed turbulence fading channel. We consider the combined effects of atmospheric turbulence, pointing error (PE), atmospheric attenuation, and angle-of-arrival (AOA) fluctuations on the channel. The communication between two legitimate peers in the presence of an external eavesdropper is studied from the perspective of communication theory security. Specifically, under two different eavesdropping scenarios where an eavesdropper is located close to the legitimate transmitter or receiver, we derive the exact closed-form expressions of the secrecy outage probability (SOP) and strictly positive secrecy capacity (SPSC), respectively. These analytical results are validated through Monte Carlo simulations. Furthermore, we also analyze the impact of various link parameters for both the main channel and the eavesdropping channel on the system performance.
To address the low accuracy of traditional stereo matching methods in depth-discontinuous and weak-texture regions, we propose an improved algorithm based on dynamic multi-feature fusion and dual-branch adaptive aggregation. A nonlinear weighting function dynamically integrates noise-resistant Re-Census, multi-directional gradient and Lab-color costs. Distinct arm extension rules are applied to weak texture and depth-discontinuous areas, enabling a dual-branch adaptive aggregation that adapts to local scene characteristics. Disparity estimation follows a winner takes all (WTA) strategy. The ultimate disparity map is generated through a region-based disparity optimization module and multiple optimization processes. Experimental results on the Middlebury dataset indicate a 16.1
The laser detection technology based on orbital angular momentum (OAM) beam has been rapidly developed, but it is restricted by many factors from being applied in practice. One of the most significant issues is that it is greatly affected by environmental conditions. When it is applied in the fog environment, the signal-to-noise ratio (SNR) of the echo signal will be seriously reduced due to the interference of environmental noise. A comprehensive study on the forward propagation and backscattering characteristic evolution theory of OAM transmission through the atmospheric fog environment is essential to formulate corresponding strategies to improve its environmental adaptability. In this work, we proposed a light field initialization method based on the acceptance-rejection method (ARM) which can convert the light field to photon flow. By combining this method with the electric Monte-Carlo (EMC), we established a propagation dynamics analysis model of OAM beam in a fog environment to reveal the propagation and evolution process of OAM beam in a complex environment. This work provides the theoretical and technical support for improving the applicability and detection accuracy of OAM laser detection technology in a complex environment. Furthermore, by combining with other models, this model can be updated for analysis of the transmission dynamics of multidimensional modulated light field under more complex environment conditions, such as foggy, smoky, and rainy environments, which can help to improve the performance of free space optical communication, Lidar, and laser energy delivery systems.
