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Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.
Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.
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Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO
The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.
A Novel Method for 3D Object Detection in Open-Pit Mine Based on Hybrid Solid-State LiDAR Point Cloud
In recent years, the mining industry has encountered challenges, such as a shortage of human resources, an ongoing emphasis on safety enhancements, and increased ecological preservation requirements. Autonomous mining trucks have emerged as a novel solution to effectively address these issues within open-pit mining operations. To meet the demanding conditions of open-pit mines, characterized by intense vibrations and extreme temperature variations, hybrid solid-state LiDAR has emerged as the primary choice for perception sensors. Recognizing the distinct data structure and distribution disparities between point clouds obtained through nonrepetitive scanning methods of hybrid solid-state LiDAR and traditional mechanical LiDAR, this paper proposed an innovative LiDAR 3D object detection model, PointPillars-HSL (PointPillars-Hybrid Solid-state LiDAR). This approach harmonizes the unique characteristics of open-pit mining environments and hybrid solid-state LiDAR point clouds. It optimizes the model’s preprocessing methodology, augments the dimensionality of pillar features, fine-tunes the loss function, and employs transfer learning techniques to reduce the reliance on specific datasets. The result is the effective deployment of a 3D object detection algorithm customized for hybrid solid-state LiDAR within the specific operational framework of open-pit mining. This achievement has yielded a noteworthy overall vehicle recognition rate of 89.72%.
Arc-Scanning Synthetic Aperture Radar for Accurate Location of Targets
The purpose of this article is to present the advantages that the use of arc-scanning synthetic aperture radar (Arc-SAR) would provide for accurate location of target to the weapon systems. Arc-SAR systems have an extraordinary capacity of angular discrimination of the targets, this fact make possible they can be used for the precise location of targets by replacing the large antennas required by the monopulse systems, used in this kind of applications, with a small rotating antenna. However, to carry out the real-time location of targets with the current processors a much more complex signal processing is needed. A simulator has been developed in this work, which allows the comparison of the theoretical and experimental results obtained by the classical systems against those obtained with the proposed Arc-SAR system working in the millimeter frequency bands. The results obtained demonstrate the ability of the proposed technique to accurately locate targets. This confirms that the obtained precisions allow the use the presented system in some important applications as Sense & Avoid (S&A) and weapon-pointing systems. Finally, possible applications of these techniques are described, especially useful is onboard sensors, because of the small size and weight of the antenna needed to implement an Arc-SAR system.
A Deep Learning Method for Building Extraction from Remote Sensing Images by Fuzing Local and Global Features
As important disaster-bearing bodies, buildings are the focus of attention in seismic disaster risk assessment and emergency rescue. It is of great practical significance to extract buildings quickly and accurately with complex textures and variable scales and shapes from high-resolution remote sensing images. We proposed an improved TransUnet model based on multiscale grouped convolution and attention named MATUnet to retain more local detail features and enhance the representation ability of global features, while reducing the network parameters. We designed the multiscale grouped convolutional feature extraction module with attention (GAM) to enhance the representation of detailed features. The convolutional positional encoding module (PEG) was added to redetermine the number of transformer, it solved the problem of local feature information loss and the difficulty of convergence of the network. The channel attention module (CAM) of the decoder enhanced the salient information of the features and solved the problem of information redundancy after feature fusion. We experimented through MATUnet on the WHU building dataset and Massachusetts dataset. MATUnet achieved the best IOU results of 92.14% and 83.22%, respectively, and achieved better than the other generalized and state-of-the-art networks under the same conditions. We also have achieved good segmentation results on the GF2 Xichang building dataset.
Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems
Smart parking systems play a vital role in enhancing the efficiency and sustainability of smart cities. However, most existing systems depend on sensors to monitor the occupancy of parking spaces, which entail high installation and maintenance costs and limited functionality in tracking vehicle movement within the car park. To address these challenges, we propose a multistage learning-based approach that leverages existing surveillance cameras within the car park and a self-collected dataset of Saudi license plates. The approach combines YOLOv5 for license plate detection, YOLOv8 for character detection, and a new convolutional neural network architecture for improved character recognition. We show that our approach outperforms the single-stage approach, achieving an overall accuracy of 96.1% versus 83.9% of the single-stage approach. The approach is also integrated into a web-based dashboard for real-time visualization and statistical analysis of car park occupancy and vehicle movement with an acceptable time efficiency. Our work demonstrates how existing technology can be leveraged to improve the efficiency and sustainability of smart cities.
Automatic Seizure Detection Using Multi-Input Deep Feature Learning Networks for EEG Signals
Epilepsy, a neurological disease associated with seizures, affects the normal behavior of human beings. The unpredictability of epileptic seizures has caused great obstacles to the treatment of the disease. The automatic seizure detection method based on electroencephalogram (EEG) can assist experts in predicting seizures to improve treatment efficiency. Epileptic seizure detection cannot be achieved accurately using the single-view characteristics of the signals. Moreover, manual feature extraction is a time-consuming task. To design a high-performance seizure identification method, automatic learning of multi-view features becomes an indispensable part for seizure detection. Therefore, the paper proposes a multi-input deep feature learning networks (MDFLN) model, which comprehensively considers the features from the time domain and the time–frequency (TF) domain for EEG signals. The MDFLN model automatically extracts the feature information of the signals through deep learning networks. Then, the bidirectional long short-term memory (BLSTM) network is used to distinguish seizure and nonseizure events. Furthermore, the effectiveness of the proposed network structure is verified in two public datasets. The experimental results demonstrate that the classification accuracy of the proposed method based on multi-view features is at least 2.2% higher than the single-view features. The MDFLN achieves better performance on CHB-MIT and Bonn datasets with accuracy of 98.09% and 98.4%, respectively. The fine-tuned model with the validation set also improves the classification performance. Compare with the state-of-the-art seizure detection methods, the multi-input deep learning network has superior competence with high sensitivity on the CHB-MIT dataset. The proposed automatic seizure detection method can reduce time consumption and effectively assist experts in the clinical diagnosis and treatment.