IEEE Sensors Journal最新文献

{"title":"The Implementation of a Compact Cold Atom Interference Gyroscope Based on Miniaturized Quartz Vacuum Chamber","authors":"Yingpeng Zhao;Dianrong Li;Jingyu Niu;Shuning Bao;Kaijun Zhang;Yuchen Wang;Bing Cheng;Cheng Zhang;Kexiao Niu;Yuanzheng Liu;Yazhou Yue;Xiaolong Wang;Bin Wu;Qiang Lin","doi":"10.1109/JSEN.2024.3483828","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483828","url":null,"abstract":"The cold atom interference gyroscope (CAIG) offer substantial potential for rotation measurement due to the high sensitivity and stability. The CAIG with a fountain configuration realized by four-pulse could provide a larger interference-loop area, and enhanced performance. However, this kind of CAIG is usually much larger and higher since it requires a vacuum chamber with sufficient height to achieve a long Raman pulse interval. We demonstrate a four-pulse CAIG based on a miniaturized vacuum chamber that enables portable and transportable rotation rate measurements. The main vacuum chamber is realized by whole glass material. The height of the vacuum unit is 0.7 m, the volume is \u0000<inline-formula> <tex-math>${{8}.{86} times {10}^{{4}}}~{{textrm {cm}}^{{3}}}$ </tex-math></inline-formula>\u0000, and the mass is 75 kg. Then, we estimated the performance of our portable CAIG in the environment of the underground laboratory and the fifth-floor office building. In the laboratory, the sensitivities of the homemade CAIG is \u0000<inline-formula> <tex-math>${4}.{44}times {10}^{-{6}}$ </tex-math></inline-formula>\u0000 rad/s\u0000<inline-formula> <tex-math>$/(text {Hz})^{1/2}$ </tex-math></inline-formula>\u0000 with an interrogation time of 55 ms and an interference area of 25 mm2. In addition, we measured the angular velocity of the Earth, the relative error is 2.4%. Furthermore, we transported the CAIG to the fifth floor of the office building, which is near the subway. Ultimately, the CAIG achieved a sensitivity of \u0000<inline-formula> <tex-math>${5}.{47}times {10}^{-{4}}$ </tex-math></inline-formula>\u0000 rad/s\u0000<inline-formula> <tex-math>$/(text {Hz})^{1/2}$ </tex-math></inline-formula>\u0000. Additionally, the theoretical maximum interrogation time achieved by this CAIG is 124 ms corresponding to a sensitivity of \u0000<inline-formula> <tex-math>${{5}.{62}times {10}^{-{8}}}~{/(text {Hz})^{1/2}}$ </tex-math></inline-formula>\u0000. Our new design of the compact CAIG could provide novel insights into the miniaturization of CAIGs, while also pointing out areas for further improvement.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"40507-40517"},"PeriodicalIF":4.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event-Triggered Distributed Fusion for Multirate Multisensor Systems Subject to Nonstationary Heavy-Tailed Noises","authors":"Xinyue Cao;Ling Zhao;Hongjiu Yang;Li Li","doi":"10.1109/JSEN.2024.3484162","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3484162","url":null,"abstract":"In this article, event-triggered distributed fusion is developed for a nonuniform multirate multisensor system with packets dropout and nonstationary heavy-tailed noises. A synchronization method is designed to facilitate estimation by transforming the nonuniform multirate multisensor system into a single-rate multisensor system. An event-triggered local estimator is proposed to obtain local estimates considering a dynamic event-triggered mechanism, nonstationary heavy-tailed noises, and packets dropout. An event-triggered distributed fusion algorithm is presented for accurate fusion estimation under reducing computational complexity. Simulation results are given to show the availability of the event-triggered distributed fusion using a single-target tracking system.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 23","pages":"39605-39616"},"PeriodicalIF":4.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plantar Planar Dual-Array Electrical Impedance Tomography (PPDA-EIT) Method for Early Diabetic Foot Ulcers’ Detection","authors":"Yunqian Wang;Hui Feng;Bo Sun;Yuru Bai;Songpei Hu;Jiafeng Yao;Tong Zhao","doi":"10.1109/JSEN.2024.3483935","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483935","url":null,"abstract":"The relationship between impedance and the concentrations of glucose and L-tyrosine (TYR) has been clarified by the plantar planar dual-array electrical impedance tomography (PPDA-EIT) for early detection of diabetic foot ulcers (DFUs). PPDA-EIT images the electrical response changes of glucose and L-TYR at different concentrations in the DFU environment. In the experiments, the characteristic frequency was determined at the maximum impedance difference (\u0000<inline-formula> <tex-math>$Delta $ </tex-math></inline-formula>\u0000Z) by the electrochemical impedance spectroscopy (EIS) method. Optimal sensor location and size were determined based on analyzing the pressure concentration area during gait. The optimum electrode size and excitation method were found through 3-D electromagnetic simulation. Pork-mimic experiments were conducted to validate the sensor design by evaluating the image correlation coefficient (ICC) and the root mean square error (RMSE). From the EIS experimental results, glucose and L-TYR, as biomarkers for early DFUs’ detection, produced the largest impedance difference (\u0000<inline-formula> <tex-math>$Delta $ </tex-math></inline-formula>\u0000Z) at a measurement frequency of 1 MHz. From the 3-D electromagnetic simulation results, the most effective excitation involved the central electrode (sixth electrode), with the optimal electrode being a square with a side length of \u0000<inline-formula> <tex-math>${D} = 8$ </tex-math></inline-formula>\u0000 mm. The PPDA-EIT sensor was able to satisfactorily obtain reconstrued images of the object with an optimal ICC =0.8110 and RMSE =0.0982, which suggests that the approach used in this study provides an accurate indication of the glucose and L-TYR concentration change region in early DFUs.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 23","pages":"39759-39770"},"PeriodicalIF":4.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lamb Wave Dispersion Compensation Based on a Fourier Basis Convolutional Autoencoder","authors":"Shuaiyong Li;Zhang Yang;Jianxin Zeng","doi":"10.1109/JSEN.2024.3483435","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483435","url":null,"abstract":"The inherent dispersion of Lamb waves will reduce the signal-to-noise ratio (SNR) and detection sensitivity of the detection signal, seriously affecting the resolution of defect identification. Therefore, it is necessary to design an effective method to reduce the influence of the dispersion effect. Traditional dispersion compensation methods are generally restricted to single-mode Lamb waves and depend heavily on manual extraction of signal characteristics; this limitation greatly reduces the generalization ability of the dispersion compensation model. In recent years, deep learning has attracted widespread attention in various fields due to its excellent adaptive feature extraction capabilities. Therefore, this article proposes a Lamb wave dispersion compensation based on a Fourier basis convolutional autoencoder (FCAE) network. Considering the frequency correlation of Lamb waves, the Fourier basis is introduced into the convolutional autoencoder (CAE) so that the network model can combine the time-frequency domain characteristics of the signal, learn the flight time of the wave packet of the dispersive signal, and reconstruct the nondispersive Lamb wave in combination with the excitation signal’s waveform. Compared with traditional dispersion compensation methods, this method can achieve dispersion compensation of Lamb waves in multimode and multiwave packet situations. Through numerical simulation and experimental verification, and comparison with various deep learning models such as convolutional neural networks (CNNs) and hole CNNs, it was verified that the proposed model still has good performance under different numbers of wave packets.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 23","pages":"39593-39604"},"PeriodicalIF":4.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Reinforcement Learning-Based Shortwave Multistation Autonomous Cooperative Direction Finding and Localization Method","authors":"Qiyue Feng;Tao Tang;Zhidong Wu;Yunpu Zhang;Ding Wang","doi":"10.1109/JSEN.2024.3483192","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483192","url":null,"abstract":"Multistation direction finding and passive localization have a wide range of applications in the fields of information countermeasures and navigation. However, signal detection and recognition based on deep learning require a lot of manual annotation, and the localization algorithm requires a large amount of computation, resulting in poor shortwave signal direction finding and localization in real time. To solve the time-consuming and labor-intensive problem, we propose a shortwave direction finding and localization method based on deep reinforcement learning (DRL). The direction of arrival (DOA) of the source signal can be used to locate the shortwave source, but the result of direction finding is not ideal in the case of the same frequency interference. In the proposed method, a space-time high-resolution processing method is designed to improve the accuracy of obtaining directional results under the complex shortwave background. The Cramér-Rao lower bound (CRLB)-based reward function is also designed, and a Markov decision process (MDP) is established. The autonomous direction finding and localization environment is trained by deep Q-network (DQN) and double-DQN (DDQN), and the comparison shows that the DDQN performs better compared with DQN. Finally, we save the online model and compare its positioning performance with other classical algorithms. The simulation experiment results and performance tests verify the feasibility of the proposed method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 23","pages":"40123-40136"},"PeriodicalIF":4.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Two-Stream Deep-Learning Network for Heart Rate Estimation From Facial Image Sequence","authors":"Wen-Nung Lie;Dao Q. Le;Po-Han Huang;Guan-Hao Fu;Anh Nguyen Thi Quynh;Quynh Nguyen Quang Nhu","doi":"10.1109/JSEN.2024.3483629","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483629","url":null,"abstract":"This article presents a deep-learning-based two-stream network to estimate remote Photoplethysmogram (rPPG) signal and hence derive the heart rate (HR) from an RGB facial video. Our proposed network employs temporal modulation blocks (TMBs) to efficiently extract temporal dependencies and spatial attention blocks on a mean frame to learn spatial features. Our TMBs are composed of two subblocks that can simultaneously learn overall and channelwise spatiotemporal features, which are pivotal for the task. Data augmentation (DA) in training and multiple redundant estimations for noise removal in testing were also designed to make the training more effective and the inference more robust. Experimental results show that the proposed temporal shift-channelwise spatio-temporal network (TS-CST Net) has reached competitive and even superior performances among the state-of-the-art (SOTA) methods on four popular datasets, showcasing our network’s learning capability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42343-42351"},"PeriodicalIF":4.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Low Concentration Water Content Detection in Biodiesel Utilizing Phase-Shift-Based Capacitive Method","authors":"Hari Sumartono;Mahfudz Al Huda;Berkah Fajar TK;Achmad Widodo;Fatih Dzulfiqar;Nacep Suryana;Azmi Muhamed;Illyas Md Isa;Suriani Abu Bakar;Ratno Nuryadi","doi":"10.1109/JSEN.2024.3483290","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483290","url":null,"abstract":"Biodiesel has become an alternative fuel to substitute conventional fuel, yet challenges remain, particularly concerning water content. The presence of water in biodiesel can negatively impact engine spray and combustion properties and may lead to damage storage tanks. To address this issue, a phase-shift-based capacitive sensor was developed in this study to accurately measure water concentration in biodiesel. The sensor, designed with a semicylindrical electrode on a glass tube, is integrated with a signal conditioning circuit and the Digilent Analog Discovery module, allowing for real-time data analysis and enhanced sensitivity in detecting low concentrations of water concentration in biodiesel. The experimental results demonstrated a direct correlation between capacitance values and phase shift, with higher capacitance leading to more significant phase shifts. A Bode plot and simulation analysis, utilizing the transfer function, validated these experimental conditions, confirming the relationship between capacitance and signal phase shift. Additionally, experiments assessing biodiesel water content revealed a proportional increase in phase-shift angle with rising water concentration, measuring water concentration in biodiesel at levels as low as 800 ppm. These findings underscore that the phase-shift magnitude in the sensor’s output reliably detects low-concentration water content in biodiesel. The performance of this sensor is particularly advantageous due to its high sensitivity, enabling the detection of low water concentrations that are crucial for maintaining biodiesel quality. Moreover, the real-time data analysis capability provided by the Digilent Analog Discovery highlights the sensor’s practicality and efficiency for on-the-spot monitoring, offering a significant improvement over conventional methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41981-41988"},"PeriodicalIF":4.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Auxiliary Branch Semisupervised Domain Generalization Network for Unseen Working Conditions Bearing Fault Diagnosis","authors":"Liang Zeng;Xinyu Chang;Jia Chen;Shanshan Wang","doi":"10.1109/JSEN.2024.3483278","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483278","url":null,"abstract":"Deep learning-based methods have made remarkable achievements in rolling bearing fault diagnosis in recent years. Nevertheless, due to the diversity and complexity of rolling bearing working conditions, how to generalize deep learning models trained under limited conditions to unseen working conditions has become a popular issue in current research. The existing methods based on domain generalization usually need to train the model using a significant quantity of labeled data under multiple known working conditions to enhance its generalization capabilities under unseen working conditions. However, the acquisition of adequately labeled samples is a time-consuming and laborious task. Therefore, this article proposes an auxiliary branching semi-supervised domain generalization network (ABSDGN). ABSDGN employs a joint learning strategy of the auxiliary and main branches. The auxiliary branch generates high-confidence pseudolabels for the unlabeled source-domain data using the labeled source-domain data. The main branch leverages both real and pseudolabels to learn domain-invariant knowledge. Meanwhile, a quadratic neuron-based convolution (QConv) is introduced to take advantage of its powerful nonlinear and higher order feature representation capabilities in enhancing the performance and generalization of the model in complicated situations. In addition, a weight decomposition method based on domain labels is proposed to decompose the main branch classifier into a generic feature and feature-specific classifier to better explore the universal knowledge among different domains. The experimental results show that the proposed model has better diagnostic accuracy and stability than the most advanced semisupervised domain generalization method on two-bearing datasets.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42327-42342"},"PeriodicalIF":4.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Frequency-Flat Sagnac Optical Fiber Microphone Based on a Composite Sensing Unit","authors":"Lai Zhang;Kun Jia;Xin Lai;Yixiao Ma;Qian Xiao;Bo Jia","doi":"10.1109/JSEN.2024.3483208","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483208","url":null,"abstract":"A frequency-flat Sagnac interferometer (SI) optical fiber microphone (OFM) based on a composite sensing unit is proposed. Based on the proposed acoustic composite-type modulation response model related to the acoustic loss coefficient \u0000<inline-formula> <tex-math>$gamma text {(} {f} text {)}$ </tex-math></inline-formula>\u0000, a composite structure consisting of a single-mode optical fiber ring, and an aspartic polyurea resin (APR) as an acoustic sensing unit, a composite OFM (COFM) based on a composite sensing unit is prepared. The acoustic tests show that compared with the conventional SI, this COFM achieves a flatter frequency response in the range of 200–6400 Hz (an average enhancement of 21.55 dB in the frequency band of 200–1000 Hz), which solves the problem of the insensitivity of the conventional SI to low-frequency signals. Besides, this COFM performs well in terms of signal-to-noise ratio (SNR), linear response of the acoustic pressure, and the response of the respective incidence angles. These results indicate that this COFM has high sensitivity and fidelity and is suitable for acoustic detection of human sound waves in the air. In addition, the composite model can be used to tune the frequency-domain characteristics of the OFM by screening composites with corresponding \u0000<inline-formula> <tex-math>$gamma text {(} {f} text {)}$ </tex-math></inline-formula>\u0000 curves according to the demand, which is promising for application.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 23","pages":"39059-39069"},"PeriodicalIF":4.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Estimation and Sensor Fault Management Using Probabilistic Voting Algorithm in UAVs","authors":"Minho Shin;Yonghyun Cho;Hungsun Son","doi":"10.1109/JSEN.2024.3483220","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3483220","url":null,"abstract":"This article presents a fault-tolerant estimator using a probabilistic voting algorithm (PVA) for the safe maneuvering of multirotor unmanned aerial vehicles (UAVs). UAVs are widely utilized in numerous applications, but any malfunction can lead to secondary accidents. The safety and robustness of the UAV component should be guaranteed to minimize fatal accidents during flight. A flight control computer (FCC) with various sensors is one of the most important components, the robustness of which should be guaranteed. In this article, a hybrid FCC including both hardware and software is developed to improve the robustness and safety of the FCC by both hardware and analytical redundancy. Triple modular FCCs for hardware redundancy are utilized to deal with various faults. The PVA is designed to estimate the reference state of the UAV and make the consensus to select the fault-free FCC by the fault probabilities of each state measurement from the FCC estimators. Moreover, multiplexers (MUXs) switch the FCC channel based on the consensus result to compensate for faults. Then, the fault identification algorithm identifies the source of the estimator faults by information on the residual signals between the estimated states and the sensor measurements. The PVA is validated through numerical simulations and experiments. This method achieves approximately a 93% correct detection rate and a fault detection time of less than 1 s, which is sufficient to maintain the dynamic responses of the UAV. These results show that the PVA improves and ensures the safe maneuvering of the UAV in various fault situations.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41010-41020"},"PeriodicalIF":4.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}