Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is Weighted K-Nearest Neighbor (WKNN) fingerprint which positions a user based on K nearest reference points measured beforehand. The challenge lies in how to configure the value of K to obtain the best positioning accuracy. In this paper, we propose a self-adaptive Weighted K-Nearest Neighbor (SAWKNN) algorithm with a dynamic K. By adjusting the value of K based on the signal strength, SAWKNN can obtain a better positioning accuracy than traditional WKNN. In particular, a significant percentage of the SAWKNN positioning makes use of a value K=1. The performance of the proposed algorithm has been evaluated in real-world experiments.

Rail surface defect inspection based on machine vision faces challenges against the complex background with interference and severe data imbalance. To meet these challenges, in this article, we regard defect detection as a key-point estimation problem and present the proposed attention neural network for rail surface defect detection via consistency of Intersection-over-Union(IoU)-guided center-point estimation (CCEANN). The CCEANN contains two crucial components. The two components are the stacked attention Hourglass backbone via cross-stage fusion of multiscale features (CSFA-Hourglass) and the CASIoU-guided center-point estimation head module (CASIoU-CEHM). Furthermore, the CASIoU-guided center-point estimation head module integrating the delicate coordinate compensation mechanism regresses detection boxes flexibly to adapt to defects’ large-scale variation, in which the proposed CASIoU loss, a loss regressing the consistency of intersection-over-union (IoU), central-point distance, area ratio, and scale ratio between the targeted defect and the predicted defect, achieves higher regression accuracy than state-of-the-art IoU-based losses. The experiments demonstrate that the CCEANN outperforms competitive deep learning-based methods in four surface defect datasets.

Visual inspection techniques for rail surface defects have become prevalent approaches to obtain information on rail surface damage. However, uneven illumination leads to illegibility of local information, and the change of the wheel-rail area results in the changeful background of the rail surface, both of which pose challenges to the visual inspection. This paper proposes a novel algorithm that detects rail surface defects via partitioned edge features (PEF). PEF eliminates the effect of uneven illumination by effectively extracting edge features and building homogeneous background on the rail surface. In the process of edge feature extraction, the thresholding based on adaptive partition of rail surface (APRS) plays an indispensable role. In APRS, the rail surface is adaptively partitioned into three types of regions according to the wheel-rail contact degree. After that, the dynamic threshold is set adaptively for each region type on the basis of the prior information of defect proportion. Subsequently, based on neighborhood information and fuzzy decision, the spatial information of adjacent pixels and the direction information of fracture edges are utilized to realize the effective recovery of incomplete defect contours. In addition, defect contours are precisely filled via a flexible combination of morphological hole filling operation and defect region extraction based on improved background difference. The accuracy of this PEF algorithm was confirmed by experiments and comparisons with related algorithms. The experiment results show that PEF detects defects with 92.03% recall and 88.49% precision, which achieves higher accuracy than the established detection algorithms for rail surface defects.

Driver distraction behavior is prone to induce traffic accidents. Therefore, it is necessary to detect them to caution drivers in time for traffic safety. In driver behavior recognition, the variety of behaviors and the diversity of the driving environment can have a certain effect on detection accuracy, and the information loss is severe in most existing methods. These make it challenging to improve the real-time accuracy of driver distraction behavior. In this paper, we propose an improved YOLOv7 based on the channel expansion and attention mechanism for driver distraction behavior detection, named CEAM-YOLOv7. The global attention mechanism (GAM) module focuses on the key information to improve accuracy. With the insertion of GAM into the Backbone and Head of YOLOv7, the global dimensional interaction features are scaled up to enable the network to extract key features. Furthermore, In the CEAM-YOLOv7 architecture, the convolution computation has been significantly simplified, which is conducive to increasing the detection speed. Combined with the Inversion and contrast limited adaptive histogram equalization (CLAHE) image enhancement algorithm, a channel expansion (CE) algorithm for data augmentation is presented to further optimize the detection effect of infrared (IR) images. On the driver distraction IR dataset of Hunan University of Science and Technology (HNUST) and Hunan University (HNU), the verification results show that CEAM-YOLOv7 achieved 20.26% higher mAP compared to the original YOLOv7 model, and the FPS reaches 156, which illustrate that CEAM-YOLOv7 outperforms state-of-the-art methods in both accuracy and speed.

The dramatic development of Internet of Things (IoT) is not only leading to the spectrum crunch but is also resulting in exorbitant energy consumption. It is thus desirable to liberate IoT from the constraint of the spectrum scarcity and to rein in the growing energy consumption. Wireless energy harvesting relay-assisted underlay cognitive networks (WEH-CRNs), which combine cognitive radio and wireless energy harvesting techniques to alleviate the spectrum and energy constraints by reusing spare spectrum for data transmissions and harvesting ambient energy for power supplies, are conceived as an efficient solution for massive IoT deployments. However, all existing works about WEH-CRNs did not take into account of the spatial location distribution of nodes, which is very important for energy harvesting and information transmission. Therefore, in this paper, we develop a framework for the design and analysis of wireless energy harvesting relay-assisted underlay cognitive networks with spatial randomly distributed nodes (WEH-RCRNs). We first propose an efficient relay selection strategy in WEH-RCRNs to determine when and which relay should be selected to assist transmission. Then, based on the proposed relay selection strategy, we derive the expression for outage probability to measure the outage performance of WEH-RCRNs. Finally, the impacts of related network parameters on the outage probability is also explored on the basis of our analysis results.

Fastener needs to be monitored and inspected periodically to ensure the rail’s safety due to its easily damaged accessory for railway infrastructure. Recently, Industrial Internet of Things (IIoT) and artificial intelligence (AI)-based visual inspection techniques have been exploited to realize the online inspection of fastener’s fault by using a fault detection IoT vehicle that is mounted with multitype sensors and cameras according to the design of our research team. However, instead of traditional artificial inspection, the AI-based automatic fastener inspection approach is still faced with some challenges, for example, collection of enough samples of faulted fastener. In this article, we propose a cascade learning embedded vision inspection method of rail fastener based on the deep convolutional neural network (DCNN). The proposed method has two steps: 1) region position and 2) fault detection. First, a modified single shot multibox detector (SSD) model is adopted to locate the fastener regions from the captured railway images. Then, a key component detection (KCD) method based on the improved faster region convolutional neural network (RCNN) is proposed to realize the detection of faulted fastener. Extensive experiments are conducted to demonstrate the performance of the proposed method. The experiment results show that the proposed method achieves an average precision of 95.38% and an average recall of 98.62% on fastener detection, which is much better than the manual operation.