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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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An Efficient Revocable Identity-Based Encryption with Equality Test Scheme for the Wireless Body Area Network
With the rapid development and popularization of cloud computing, people are willing to upload their own data to the cloud to enjoy the services. However, some personal and private data are not suitable for uploading directly to the cloud. Therefore, these data must be encrypted before uploading to the cloud to ensure the confidentiality. To achieve the confidentiality of data and enjoy cloud services, a notion of identity-based encryption with equality test (IBEET) was proposed. Using IBEET, two ciphertexts encrypted under different public keys can be tested to confirm whether they contain the same plaintext. The equality test can be applied to the wireless body area network system in which the cloud can utilize ciphertexts from patients and medical institutions to perform equality tests to determine whether which patient’s status is abnormal. Indeed, revoking illegal or expired users on any cryptosystem is an important issue. To the best of our knowledge, there is little research on the design mechanism of user revocation in the IBEET. In this paper, we propose a novel notion of revocable identity-based encryption with an equality test, called RIBEET. Based on the notion, we present the first RIBEET scheme. Meanwhile, the proposed scheme will be proven to be secure under the bilinear Diffie-Hellman (BDH) assumption.
Analysis of the Influence of Slope Sliding on the Stability of Underground Diaphragm Wall Bridge Foundation Based on Wireless Sensor Network
Real-time monitoring, condition assessment, early warning processing, and damage identification of underground diaphragm wall bridge structures are the current research trends. Based on the wireless sensor network theory, this paper constructs the stability model of the slope sliding on the underground diaphragm wall bridge foundation. The model builds a complete set of underground diaphragm wall bridge vibration signal acquisition and monitoring platform through wireless sensor network node data acquisition and solves the problem of data accuracy measurement by using theoretical analysis, software and hardware design, software simulation, and experimental verification methods. During the simulation process, experiments were designed such as slope sliding vibration signal acquisition data accuracy test, sensor node wireless charging power test and modeling, lithium battery charging power test and modeling, wireless rechargeable sensor node system work test, and other experiments. The experimental results show that the accuracy of the collected vibration signals of the underground diaphragm wall bridge is high, the main working frequency bands are 868 MHz, 915 MHz, and 2.4 GHz, the maximum data transmission rate is 250 Kbps, and the communication distance reaches 100 m, which can meet the requirements of the underground diaphragm wall. The power of wireless charging of lithium batteries reaches 4.5 mW, which effectively improves the stability measurement accuracy of underground diaphragm wall bridge foundations.
A Robust and Lightweight Detector for Ship Target with Complex Background in SAR Image
Accurate target detection technology on ships can improve the comprehensive perception ability of weapon equipment. For SAR ship target detection in complex environments, false and missing alarms are serious. We design a new real-time ship target detection algorithm 3S-YOLO in SAR images. Firstly, reconstruct the network structure, adjust the relationship between receptive field and multiscale fusion, and realize the lightweight processing of feature extraction network and feature fusion network. Then, the network is pruned and compressed by the FPGM pruning algorithm to accelerate the reasoning speed. Finally, the Varifocal-EIoU loss function is designed to balance the positive and negative samples and overlapping losses and highlight the contribution of positive samples. To verify the effectiveness of the 3S-YOLO algorithm, verification is carried out in public datasets SSDD and HRSID. The results show that the accuracy of the model can be improved to 99.2% and 95.6%, respectively, after optimization. After pruning, the model volume decreased significantly and could be compressed to 190 KB. Model reasoning time can be reduced to less than 3 ms. Compared with the current mainstream algorithms, 3S-YOLO has achieved good results in all aspects to meet the real-time ship target detection in SAR images.
On Study of 1D Depth Scans as an Alternative Feature for Human Pose Detection in a Sensor Network
Inspired by the notion of swarm robotics, sensing, and minimalism, in this paper, we study and analyze how a collection of only 1D depth scans can be used as a part of the minimum feature for human body detection and its segmentation in a point cloud. In relation to the traditional approaches which require a complete point cloud model representation for skeleton model reconstruction, our proposed approach offers a lower computation and power consumption, especially in sensor and robotic networks. Our main objective is to investigate if the reduced number of training data through a collection of 1D scans of a subject is related to the rate of recognition and if it can be used to accurately detect the human body and its posture. The method takes advantage of the frequency components of the depth images (here, we refer to it as a 1D scan). To coordinate a collection of these 1D scans obtained through a sensor network, we also proposed a sensor scheduling framework. The framework is evaluated using two stationary depth sensors and a mobile depth sensor. The performance of our method was analyzed through movements and posture details of a subject having two relative orientations with respect to the sensors with two classes of postures, namely, walking and standing. The novelty of the paper can be summarized in 3 main points. Firstly, unlike deep learning methods, our approach would require a smaller dataset for training. Secondly, our case studies show that the method uses very limited training dataset and still can detect the unseen situation and reasonably estimate the orientation and detail of the posture. Finally, we propose an online scheduler to improve the energy efficiency of the network sensor and minimize the number of sensors required for surveillance monitoring by employing a mobile sensor to recover the occluded views of the stationary sensors. We showed that with the training data captured on 1 m from the camera, the algorithm can detect the detailed posture of the subject from 1, 2, 3, and 4 meters away from the sensor during the walking and standing with average accuracy of 93% and for different orientation with respect to the sensor by 71% accuracy.
Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis
Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET, which can better initialize the size and position of the head detection frame in the image with the help of density map and RGBD-adaptive perception network. Results. The results show that our method achieves the best performance in RGBD dense video crowd counting on five labeled sequence datasets; the MICC dataset, CrowdFlow dataset, FDST dataset, Mall dataset, and UCSD dataset were evaluated to verify its effectiveness. Conclusion. The experimental results show that the proposed DRD-NET model combined with DRC-ConvLSTM outperforms the existing video crowd counting ConvLSTM model, and the effectiveness of the parameters of each part of the model is further proved by ablation experiments.
A Novel Portable Soil Water Sensor Based on Temperature Compensation
Soil water sensors based on the standing wave rate (SWR) principle are affected by temperature in long-term operation. To address this problem, a temperature compensation model based on the binary regression analysis method is proposed. The measurement results of the temperature-compensated standing wave rate (TCSWR) sensor at different temperatures and soil volumetric water content are analyzed, and the least-squares principle is used to identify the parameters to be determined in the compensation model for temperature for the SWR soil water sensor. A portable tapered TCSWR sensor with built-in temperature compensation model was developed on this basis. The calibration results show that the standing wave measurement circuit of the TCSWR sensor can effectively respond to changes in soil water, and the coefficient of the fitted equation exceeds 0.95. A comparison of the results before and after temperature compensation proves that compensation can significantly reduce the measurement error of the TCSWR sensor and improve the measurement accuracy. The static and dynamic characteristics of the TCSWR sensor show that the measurement range of the TCSWR sensor is7.50%-31.50%, the measurement accuracy is ±0.63%, the stability is good, the resolution is a minimum of 0.05%, and the dynamic response time is less than 1 s. The absolute error of the TCSWR sensor measurement is less than 1% in comparison with similar sensors, demonstrating that the measurement results of the TCSWR sensor are reliable.