Despite the high demand for Internet location service applications, Wi-Fi indoor localization often suffers from time- and labor-intensive data collection processes. This study proposes a novel indoor localization model that utilizes fingerprinting technology based on a convolutional neural network to address this issue. The aim is to enhance Wi-Fi indoor localization by streamlining the data collection process. The proposed indoor localization model leverages a 3D ray-tracing technique to simulate the wireless received signal strength intensity (RSSI) across the field. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of Wi-Fi indoor localization. In addition, an RSSI heatmap fingerprint dataset generated from the ray-tracing simulation is trained on the proposed indoor localization model. To optimize and evaluate the model’s performance in real-world scenarios, experiments were conducted using simulated datasets obtained from the publicly available databases of UJIIndoorLoc and Wireless InSite. The results show that the new approach solves the problem of resource limitation while achieving a verification accuracy of up to 99.09%.

Mask is considered as an important prior for fusion, which could selectively enhance specific regions to generate ideal fused images. However, masks used in the existing methods exhibit limitations in the precise representation of targets, and more importantly, these masks are generated from a single modality, which restricts the effective integration of multi-modal information. To address this issue, we propose a competitive mask-guidance fusion method for infrared and visible images. A multi-modal semantic-sensitive mask selection network is proposed to generate complementary-mask maps, which organically integrate advantageous target regions of different modalities by competitively comparing the qualities of masks. In this network, a pseudosiamese architecture is designed to obtain respective target masks, and specifically, a spatial-aligned-based feature aggregation module is devised to produce high-quality pseudo-labels which are served as references for the generation of the complementary-mask maps. Furthermore, we propose a bidirectional-collaboration region fusion strategy, which enhances the expression of advantageous target regions from each modality in foreground while suppressing the contribution of corresponding regions from the other modality in background. Compared to methods on public datasets, the results show that our method significantly enhances the description of semantic-sensitive targets in fused images, including the saliency and the integrity of structural information. Code are available at https://github.com/9DunDun9/FusionNet.git.

Surface defect detection plays a quality assurance role in industrial manufacturing processes. However, the diversity of defects and the presence of complex backgrounds bring significant challenges to salient object detection. To this end, this study proposes a new adaptive multi-scale feature fusion network (AMSFF-Net) to solve the SOD problem of object surface defects. The upsampling fusion module used adaptive weight fusion, global feature adaptive fusion, and differential feature adaptive fusion to fuse information of different scales and levels. In addition, the spatial attention (SA) mechanism was introduced to enhance the effective fusion of multi-feature maps. Preprocessing techniques such as aspect ratio adjustment and random rotation were used. Aspect ratio adjustment helps to identify and locate defects of different shapes and sizes, and random rotation enhances the ability of the model to detect defects at different angles. The negative samples and non-uniform-distribution samples in the magnetic tile defect dataset were further removed to ensure data quality. This study conducted comprehensive experiments, demonstrating that AMSFF-Net outperforms existing state-of-the-art technologies. The proposed method achieved an S-measure of 0.9038 and an $F_{\mathsf{\beta }}^{\mathrm{m}\mathrm{a}\mathrm{x}}$ of 0.8782, which represents a 1% improvement in $F_{\mathsf{\beta }}^{\mathrm{m}\mathrm{a}\mathrm{x}}$ compared to the best existing methods.

Autonomous defect detection is critical for intelligent industrial production lines. Pipeline leakage as a typical defect presents an irregular or inconspicuous appearance, resulting in the failure of general visual inspection approaches in actual applications. To this end, we propose a contactless pipeline leak detection system with a novel semantic segmentation network, namely, WDA-Net, that can work in various illumination conditions. A novel convolutional neural network (CNN)–Transformer hybrid architecture in the encoder is designed to capture defect features, especially for features with weak appearance, while an efficient fusion strategy that integrates adjacent feature maps and gradually recovers the spatial details by skip connections is proposed in the decoder. Except for the categories and intrinsic characteristics of defects, complex illumination conditions have been considered in the constructed dataset. Comprehensive experiments demonstrate that the proposed method outperforms other methods, achieving 70.0% mean intersection over union (mIoU) on the pipeline leakage defect (PLD) dataset and 84.6% mIoU on the NEU-Seg dataset. The code is available at https://github.com/zbl929/WDA-Net .

The fusion of multiple sensor data to improve positioning accuracy and robustness is an important research direction in indoor positioning systems. In this paper, a Wi-Fi- and vision-based Fusion Adaptive Kalman Filter (FAKF) method is proposed for improving the accuracy of indoor positioning. To improve the accuracy of Wi-Fi positioning, a random forest algorithm with added region restriction is proposed. For visual positioning, YOLOv7 target detection and Deep SORT target tracking algorithms are combined in order to improve the stability of visual positioning. The fusion positioning method proposed in this study uses Kalman filtering for state estimation and updating by combining measurements from camera and Wi-Fi sensors, and it adaptively adjusts the parameters and weights of the filters by monitoring the residuals of the camera and Wi-Fi measurements in real time in order to optimize the accuracy and stability of the position estimation. In the experimental section, the real trajectory data and the predicted trajectory data generated using different positioning methods are compared. The experimental results show that the fused positioning method can significantly reduce positioning errors and the fused data can more accurately reflect the actual position of a target compared with single-sensor data.

Due to the fast advancement in wireless communication technology, the demand for the modeling and generating of wireless channels is increasing. Deep learning technology is gradually applied in the wireless communication field, and the Generative Adversarial Network (GAN) framework provides a new solution for channel modeling. This paper presents a method based on Wasserstein GAN with gradient penalty (WGAN-GP) guided by an attention mechanism for wireless channel modeling and generating. The feature extraction capability of the model is enhanced by adding a channel attention mechanism in WGAN-GP, and the representation capability of the model is enhanced by adaptively recalibrating the channel feature response. The experimental results demonstrate that the proposed approach accurately models the channel distribution and generates data that closely aligns with the real channel distribution. The proposed method has been shown to achieve superior qualitative and quantitative evaluation compared to the existing method.