IEEE Sensors Journal最新文献

{"title":"High-Sensitivity Photoacoustic Spectroscopy Gas Sensor Enhanced by a Microphone-Array-Coupled Differential Resonator","authors":"Fupeng Wang;Jianguo Zhang;Peixing Cui;Liyan Fu;Qiang Wang","doi":"10.1109/JSEN.2024.3488816","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3488816","url":null,"abstract":"A high-sensitivity photoacoustic spectroscopy (PAS) gas sensor based on a differential photoacoustic cell (DPAC) and microphone array is reported in this article. The DPAC, with an inner volume of only 4.3 mL, was especially designed to couple with 40 microphones to enhance photoacoustic detection. Finite element analysis (FEAs) was used to optimize the acoustic characteristics inside the DPAC and then determine the distribution of the microphone array. For assessing the performance, an acetylene (C₂H₂) PAS sensor was developed using a 1531 nm distributed feedback (DFB) laser with an optical power of 30 mW, operating in the mode of wavelength modulation spectroscopy (WMS). Compared to the sensor with a single microphone, the microphone array realized 39.2 times photoacoustic enhancement and 13.7 times improvement in signal to noise ratio (SNR). The minimum detection limit (MDL) of the developed C₂H₂ PAS sensor can be improved to 8.6 ppb when the average time is extended to 754 s, which is equal to a normalized noise equivalent absorption (NNEA) of 3 × 10−10 cm−10 W/Hz1/2.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41924-41931"},"PeriodicalIF":4.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844418","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":"Polarized Compass Orientation Framework Based on Real-Time Discrimination of Heading Information in Harsh Weather","authors":"Donghua Zhao;Nan Li;Bo Wang;Jinguang Yue;Qing Zhang;Chong Shen","doi":"10.1109/JSEN.2024.3489612","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3489612","url":null,"abstract":"How to achieve stable and reliable heading information in the case of harsh weather environments and global satellite navigation system (GNSS)-denied scenarios is more challenging for autonomous navigation. In recent years, the bioinspired polarized light orientation has been extensively and deeply explored in the field of autonomous navigation due to its high reliability and strong anti-interference performance. Herein, a novel polarized compass orientation framework in terms of real-time heading information discrimination is skillfully formulated and included in monitoring heading signal changes to characterize navigation behaviors without training complex neural networks or accumulating a large amount of data to drive. This study aims to solve the problem of the polarization compass orientation error caused by external environmental and internal device noise. Different from existing research, an optimized live heading signal discriminating and monitoring strategy based on sparse shrinkage local Fisher discriminant analysis (OSLFDA) is investigated in this article to distinguish the different data states comprised of pure heading signal and mixed noise without model traversal. Particularly, a discriminant function is calculated by formulating a heading data state discriminant matrix with the weight coefficient to maximize interclass and minimize intraclass separation. On this basis, an effective noise-suppressing algorithm based on a modified sliding singular spectrum analysis (MSSA), similarly without the need for a large amount of training neural networks, is presented to attenuate the noise in identified noisy components by OSLFDA so as to preserve the true heading signal to the maximum extent. Various representative experiment results demonstrate the efficacy and feasibility of the polarized compass orientation framework for drone navigation. In the drone-mounted test experiments on dust days, the root-mean-square error (RMSE, 0.5116°) is reduced by more than 81% when compared to the current up-to-date typical polarized light orientation techniques.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41136-41147"},"PeriodicalIF":4.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859208","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":"Label-Free Ultrasensitive Cholesterol Detection Based on SPR Optical Fiber Sensor","authors":"Hongxin Zhang;Xuegang Li;Xue Zhou;Yanan Zhang","doi":"10.1109/JSEN.2024.3489622","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3489622","url":null,"abstract":"A sensitivity-improved fiber-optic sensor based on surface plasmon resonance (SPR) is proposed, and a strategy [using polyacrylic acid (PAA) and chitosan] to fix carbon nanotubes (CNTs) on the probe to detect cholesterol is established. CNTs can improve the sensitivity of the sensor. The sensitivity of the sensor after coating with CNTs is 2437 nm/RIU, which increases by 65% compared with the uncoated sensor. \u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000-cyclodextrin (\u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000-cd) can recognize cholesterol molecules. First, the traditional 11-mercaptoundecanoic acid (MUA) method was used to fix \u0000<inline-formula> <tex-math>$beta $ </tex-math></inline-formula>\u0000-cd on the sensor, and cholesterol sensitivity is 0.023 nm/nM in the range of 10–300 nM. Then, the Langmuir adsorption isotherm model was used to analyze the detection of cholesterol by the new method. Experiment results show that cholesterol sensitivity was 0.065 nm/nM at 10–400 nM (2.83 times than the MUA method), and the limit of quantification (LOQ) was 2.34 nM. In addition, the sensor has expanded detection range, improved the sensitivity of the sensor, and also has good stability and selectivity, paving the way for the future use of fiber sensors in cholesterol detection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"40972-40978"},"PeriodicalIF":4.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844587","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":"Flowthrough Balance for Reaction Control Testing","authors":"Kenneth G. Toro;Devin E. Burns;Peter A. Parker","doi":"10.1109/JSEN.2024.3489423","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3489423","url":null,"abstract":"Atmospheric entry vehicles undergo wind tunnel testing during development and the flight certification process. During testing, reaction control system (RCS) thrusters are simulated using high-pressure fluid to validate RCS design and enable developing controller algorithms. Traditionally, flowing high-pressure fluid through a six-component balance has been impractical using conventional manufacturing, and has limited force measurement to five components (no axial force). A new six-component flowthrough balance enabled by additive manufacturing was developed, which is suited for RCS ground testing fluid pressures up to 13.8 MPa (2000 psi). Multiple prototypes with compliant flow passages were designed and fabricated to enable axial force measurements. One selected design was fully fabricated, instrumented, and evaluated. Results confirmed finite element analysis (FEA) predictions of gauge outputs, flow passage compliance, and successfully demonstrated the high-pressure flowthrough concept.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"40575-40580"},"PeriodicalIF":4.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844502","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":"High-Throughput Separation of Alexandrium Cells Based on Deterministic Lateral Displacement Arrays With Different Post Shapes","authors":"Yanjuan Wang;Jiayue Liu;Jun Zhao;Gege Ding;Jie Wen;Yuxian Yan;Junsheng Wang","doi":"10.1109/JSEN.2024.3486150","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3486150","url":null,"abstract":"Alexandrium is a common red tide alga, and most incidents of poisoning are attributed to it. Therefore, the research on Alexandrium is very crucial for mitigating red tide disasters and safeguarding the marine environment. However, Alexandrium algae cells in natural environments or cultures are frequently contaminated by impurities or hybrid algae. Therefore, the separation and purification of Alexandrium cells are the essential prerequisites for further research. Herein, a high-throughput microalgae cell separation method based on the principle of deterministic lateral displacement (DLD) has been proposed, which can achieve high-throughput separation without additional external force required. First, the relationship between the micropillar shapes, chip parameters, and the separation threshold was analyzed. Based on this, two types of chips with different micropillar shapes, circular and triangular, were designed. Then, three types of algae cells, Alexandrium, Chlorella, and Thalassiosira weissflogii, were selected as the samples. The experimental results indicate that both chips can achieve satisfactory separation performance, with a separation efficiency exceeding 80% and a maximum throughput of \u0000<inline-formula> <tex-math>$300~mu $ </tex-math></inline-formula>\u0000L/min. Furthermore, the performance of the two chips was compared and analyzed in terms of separation efficiency, throughput, flow resistance, and stability. The results indicate that the triangular micropillar chip outperforms the circular micropillar chip in the above aspects. To our knowledge, this is the first time that DLD technology has been applied to the separation and purification of Alexandrium cells. These findings will contribute to the research and utilization of Alexandrium cells, red tide disaster prevention, and marine ecological environment protection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"40358-40366"},"PeriodicalIF":4.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858764","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 Augmented State Robust Unscented Kalman Filtering for Gravity Gradient Matching Navigation","authors":"Duanyang Gao;Xu Lyu;Yunhai Zhong;Hongjin Zhou","doi":"10.1109/JSEN.2024.3488733","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3488733","url":null,"abstract":"As a completely autonomous and passive positioning method, gravity gradient matching can provide high-precision positioning services for submarines during long underwater voyages, holding great military application prospects. Aiming at the problem of measurement disturbances in the gravity gradiometer, an augmented state robust unscented Kalman filtering (ASRUKF) gravity gradient matching algorithm is proposed. This algorithm combines the Huber-based robust unscented Kalman filter (RUKF) and the augmented state unscented Kalman filter (ASUKF) to automatically address various interference scenarios, such as measurement biases, discrete outliers, and mixture Gaussian distributions. It compensates for the limitations of single algorithms and exhibits stronger practicality. Ship experiment results demonstrate that the proposed algorithm can effectively mitigate the impact of colored noises, including measurement biases, discrete outliers, and mixture Gaussian distributions, significantly improving the navigation performance of the system. Additionally, the effects of data-sampling periods and gradiometer noise levels on the system are also evaluated.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41912-41923"},"PeriodicalIF":4.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844501","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":"Diagnosis of Incipient Electrical Defects on Long Cables Using a Chaotic Duffing Oscillator","authors":"Fabrice Auzanneau","doi":"10.1109/JSEN.2024.3487953","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3487953","url":null,"abstract":"Electrical wires are always present in critical modern systems, for the transmission of energy and signals. Due to aging or harsh environmental conditions, cables can develop defects that can reduce their performance and create severe problems. Reflectometry-based methods have been studied and implemented over the last 30 years and have shown good performance in detecting faults in cable networks. They rely on the analysis of echoes created by faults on the cable, and can detect defects whose echoes are a few percent of the input signal. But these methods are limited when it comes to detecting incipient faults characterized by echoes one order of magnitude smaller. This article presents the proof of concept of an entirely different diagnosis method. The method proposed here does not analyze the signal returned by the cable but uses this signal to disturb a system in an unstable equilibrium position. A chaotic Duffing oscillator is coupled to the cable under test and a test signal is sent into the cable: the returned signal is added to the oscillator control signal. The system parameters are chosen so that the oscillator is at the limit of stability, and a very slight disturbance of the cable causes a change in behavior that is much easier to detect than a very small peak sometimes drowned in noise on a reflectogram. The sensitivity of this new method is higher than that of state-of-the-art ones, enabling it to detect faults of signature of 1% or less of the amplitude of the test signal in long cables.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41073-41080"},"PeriodicalIF":4.3,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859179","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 Low-Altitude UAV Dataset Based on Visual–Inertial Navigation","authors":"Pin Lyu;Chengyou Yong;Jizhou Lai;Cheng Yuan;Qianqian Zhu;Adong Han","doi":"10.1109/JSEN.2024.3488289","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3488289","url":null,"abstract":"As unmanned aerial vehicles (UAVs) are increasingly utilized in operational domains ranging from 50 to 500 m, there is a growing need for robust and reliable autonomous positioning, particularly in scenarios where satellite signals are unavailable. To address this challenge, we present a set of visual–inertial datasets collected by low-altitude UAVs. The dataset includes stereo cameras, an inertial measurement unit (IMU), a laser ranging sensor, and RTK-based ground truth. All these sensors have been calibrated and synchronized. The dataset consists of 28 sequences captured across diverse scenarios. To the best of our knowledge, this is the first UAV visual navigation dataset to encompass such a wide range of flight altitudes. Furthermore, we have evaluated the dataset using state-of-the-art visual–inertial simultaneous localization and mapping (SLAM) algorithms. The results demonstrate that existing solutions perform inadequately in certain situations. For the benefit of the research community, we make this dataset publicly accessible. The webpage of our dataset is \u0000<uri>https://github.com/boomzoeycy/A-Low-Altitude-UAV-Dataset-based-on-Visual-Inertial-Navigation</uri>\u0000.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41904-41911"},"PeriodicalIF":4.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844368","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 Fusion of Graph- and Grid-Based Hybrid Model of Object Detection and Semantic Segmentation for 4-D Millimeter-Wave Radar","authors":"Hongyan Wang;Zifeng Huang;Jiakang Ma;Huimei Feng","doi":"10.1109/JSEN.2024.3479214","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3479214","url":null,"abstract":"The next-generation 4-D millimeter-wave radar can provide rich information and dense point cloud and perceive the environment under all-weather and all-operating conditions, making it very suitable for autonomous driving systems. However, the existing object detection and semantic segmentation models based on 4-D millimeter-wave radar are usually directly transplanted from light laser detection and ranging (LiDAR), which cannot effectively adapt to the millimeter-wave radar point cloud features, resulting in poor detection performance. Concerning this, a hybrid model for 4-D millimeter-wave radar object detection and semantic segmentation is developed here via fusing graph and grid. A graph neural network (GNN) module named radar adaptive multichannel GNN (RAMGNN) is first designed, which leverages topology graphs and feature maps to propagate and update point cloud features. The node embeddings outputted from RAMGNN can be directly used for semantic segmentation and serve as a point cloud feature encoder for subsequent object detection. In what follows, the point cloud is projected onto a 2-D bird’s-eye view (BEV) grid, and its multiscale features can be extracted exploiting a backbone network with channel attention mechanism. Finally, multiscale features are fused to achieve effective object detection and semantic segmentation. Experimental results conducted on the publicly available dataset view-of-delft (VoD) demonstrate that the proposed model outperforms state-of-the-art algorithms in terms of both object detection performance and semantic segmentation quality.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42268-42280"},"PeriodicalIF":4.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142842001","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 Extending Interclass Distance Real-Time Network Using Positional Orientation Transformation for Few-Shot Strip Steel Surface Defect Classification","authors":"He Zhang;Han Liu;Runyuan Guo;Qing Liu;Lili Liang;Wenlu Ma;Ding Liu","doi":"10.1109/JSEN.2024.3488000","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3488000","url":null,"abstract":"In the era of intelligent manufacturing, the rapid and accurate classification of strip steel surface defects is crucial. Deep learning typically relies on a large number of parameters and labeled samples to achieve outstanding performance. However, acquiring a sufficient number of defects in actual steel production poses challenges, and a high number of parameters can impact the real-time performance of defect classification. To tackle these issues, an extending interclass distance (Eid) real-time network using positional orientation transformation for few-shot strip steel surface defect classification is proposed (called the EidNet). EidNet utilizes a fewer parameters neural network as the feature extractor, enabling quick model convergence. To overcome the potential limitations of the fewer parameters model in expressing features, EidNet employs a no learnable parametric technique to artificially extend the interclass distance in the metric space, utilizing directional transformation of prototype positions to manually design the direction of extending, dispersing prototypes that are different from the query sample class as much as possible, thereby enhancing classification performance. The model uses the Euclidean distance as its classifier to maintain a low overall number of parameters. Experimental results demonstrate that EidNet significantly enhances the real-time performance of defect classification, striking a balance between real-time requirement and classification accuracy, while also exhibiting superior generalization capabilities.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42523-42537"},"PeriodicalIF":4.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844323","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}