IEEE Sensors Journal最新文献

{"title":"Research on the Imaging and Accuracy Analysis for Fish Head Detection by Using Directional Borehole Radar","authors":"Xiaosong Zhu;Xianlei Xu;Suping Peng;Fangyi Liu;Peng Liang","doi":"10.1109/JSEN.2025.3579876","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3579876","url":null,"abstract":"Oilfield casing is crucial for effective oil extraction and storage. Casing failures often occur at various depths during drilling, significantly affecting production. Conventional geophysical methods are inadequate in drilling environments, posing challenges for accurate casing failure detection. This article presents a directional detection technique for “fish head” identification using borehole radar, alongside imaging and accuracy analysis. The working principle of the fish head radar detection system is discussed, including necessary hardware and software components. A pulsewidth modulation (PWM) control algorithm enables omnidirectional data acquisition in “blind hole” conditions. The study investigates the impact of radar rotation speed and detection movement speed on results. Utilizing a gray-level co-occurrence matrix, the analysis focuses on features such as energy, contrast, homogeneity, and correlation to quantitatively assess the pipeline response area in radar images, identifying optimal rotation and movement speeds of 0.12 m/s and a PWM duty cycle of 50%–70%. Field experiments for fish head detection were conducted in the Daqing oilfield with two radar antennas of different frequencies. Results show a deviation between the set and actual antenna angles within 1°, achieving an accuracy of 95.5%. Casing breaks were detected at 0° and 235° in the simulated well, with maximum detection depths of 6 m at 500 MHz and 25 m at 100 MHz. These findings validate the capability of borehole radar omnidirectional scanning for precise anomaly detection around oil wells, providing technical support for identifying casing break orientations.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29772-29784"},"PeriodicalIF":4.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751005","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 Large-Range Graphene Pressure Sensor With Membrane-Beam Structure","authors":"Junqiang Wang;Pengcheng Li;Haiyang Li;Ningning Su;Yonghong Cao","doi":"10.1109/JSEN.2025.3579595","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3579595","url":null,"abstract":"A large-range graphene pressure sensor with a membrane-beam hybrid structure was designed and fabricated, and its measuring range could reach 200 MPa. The membrane made of high-strength alloy steel was used as the elastic element of the sensor. The graphene-based sensitive element was protected by Si3N4 and distributed at the root region of the silicon chip’s beam. The reliable connection between the steel membrane and the silicon chip was realized by solder bonding technology using Sn99Ag0.3Cu0.7. Scanning electron microscopy (SEM) microstructural analysis confirmed defect-free interconnection at the weld interface. Moreover, the results of SEM, Raman spectroscopy, and I–V tests showed that the graphene sensitive element has high quality and stable resistance. The pressure test indicated that the range of the sensor had achieved up to 200 MPa, the graphene gauge factor (GF) value is 1.27, and the sensitivity is 3.39 mV/MPa. According to comparation, this article has developed a large-range graphene pressure sensor, which will advancement facilitate the development and application of graphene in specialized fields, such as aerospace, industrial control, and the petrochemical industry.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29765-29771"},"PeriodicalIF":4.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750894","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":"Hemorrhagic Brain Strokes Detection Using Recurrent Neural Networks-Based Microwave Imaging Technique","authors":"Ammar Fawzi AlQasem;Muhammad Firdaus Akbar;Younis Mahmood Abbosh;Muthukannan Murugesh","doi":"10.1109/JSEN.2025.3579591","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3579591","url":null,"abstract":"This research aims to use a microwave imaging system, leveraging deep learning, to detect hemorrhagic strokes by extracting effective features from backscattered signals of the head. This method is promising for early diagnosis and avoiding surgical intervention, being low-cost, portable, nonionized, real-time imaging, comfortable and harmless. Microwave imaging detects hemorrhagic strokes by exploiting the contrast in dielectric properties between healthy and unhealthy tissues. The proposed method was simulated using Computer Simulation Technology (CST) software, featuring two opposite antipodal Vivaldi antennas, positioned 20 mm from the head model with a gain greater than 7 dBi in the 2.55 GHz band. The antennas act as transmitters and receivers. The head model was created using a realistic human voxel model, with hemorrhagic strokes simulated by using the electrical properties of blood. The proposed model was experimentally verified and fabricated using chemical materials. Data collected from the model included four time-domain and eight frequency-domain features. Three-layer recurrent neural networks (RNNs) were trained using time features, frequency features, and a combination of both. This approach was successful, achieving 100% accuracy in detecting the presence or absence of strokes for simulated and experimental data and 90% accuracy of a 5 mm stroke with improved discrimination of stroke size and localization when using combined time-frequency features for experimental data while the lowest error in xy plane is 13.69 mm. The results are highly encouraging, supporting the development of portable equipment for brain stroke detection.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29752-29764"},"PeriodicalIF":4.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751106","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-Precision 3-D Mapping in Tunnels Based on Vibration Magnitude-Adaptive Kalman Filtering","authors":"Yaodong Song;Xiaobing Zheng;Ying Zhu","doi":"10.1109/JSEN.2025.3578965","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578965","url":null,"abstract":"The 3-D digital map of tunnel walls is crucial for the precise control of automated shotcrete operations in tunnels. The accuracy of pose estimation for point cloud sensors directly affects the precision of the 3-D digital map, especially in dynamic vibration environments, where time-varying noise interference poses significant challenges. This study proposes a vibration-adaptive Kalman filtering (VAKF) framework that integrates total station (TS), inertial measurement unit (IMU), and motion constraints for high-precision pose measurement of the tunnel boring machine (TBM) shotcrete arm. The method employs a two-level fusion strategy: attitude-level fusion dynamically adjusts the noise covariance of the IMU and TS based on vibration magnitude to correct the angle; position-level fusion embeds circular motion constraints to suppress cumulative integration errors. Experiments conducted in a simulated tunnel environment demonstrate that the proposed method achieved an RMSE of 34.87 mm under the working conditions of an arm length of 1 m and a movement angular velocity of 0.1 rad/s, outperforming the measurement accuracy of static TS measurement, IMU integration measurement, and Sage-Husa adaptive filtering. Additionally, this method maintains robust performance across different arm lengths and movement speeds, with the RMSE consistently remaining below 41.539 mm. This study addresses the limitations of the existing sensor fusion methods in vibration scenarios, providing a practical solution for real-time 3-D mapping in automated tunnel construction.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29722-29735"},"PeriodicalIF":4.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11040144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Denoising Method for Ultrasonic Echo Signal of Mining Well Logging Instrument Based on NRBO-ICEEMDAN Wavelet Thresholding","authors":"Maoyong Cao;Yunlong Hua;Jinfeng Zhang;Hui Zhang;Fengying Ma;Peng Ji","doi":"10.1109/JSEN.2025.3578599","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578599","url":null,"abstract":"To effectively deal with the problem of high noise and low signal-to-noise ratio (SNR) in the ultrasonic echo signal caused by mud pairs when the ultrasonic well logging instrument operates in the underground complex environment, this article proposes a wavelet threshold denoising method based on Newton-Raphson-based optimizer (NRBO)-improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). This method employs the NRBO algorithm to optimize the parameters of ICEEMDAN, targeting the optimal combination of white noise amplitude weights (Nstd) and the number of noise additions (NE). Then with the help of correlation coefficient method to filter out the effective components from the intrinsic mode functions (IMFs) obtained from the decomposition, the improved wavelet threshold function is used to suppress the noise of the signal components, and finally, the denoised components are reconstructed to constitute the denoised signal. The results indicate that the proposed method demonstrates superior performance over conventional signal denoising techniques. Compared with the ICEEMDAN algorithm, it achieves a 23.45% improvement in SNR and a 38.27% reduction in root-mean-square error (RMSE). This approach effectively enhances signal clarity, thereby substantially improving the reliability and measurement accuracy in ultrasonic logging applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29700-29710"},"PeriodicalIF":4.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750944","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":"Core-Sheath Graphene Yarn Sensors for Versatile Wearable Pressure Monitoring","authors":"Zhengwei Jia;Xiaoping Lin;Hao Liu","doi":"10.1109/JSEN.2025.3579215","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3579215","url":null,"abstract":"To address the demand for high-performance flexible pressure sensors in wearable electronics, this study proposes a fabrication method for graphene-wrapped yarn piezoresistive sensors with a core-sheath structure. Using a stainless-steel sewing thread as the conductive core and graphene-coated conductive nylon monofilament as the functional wrapping layer, the yarn twist parameters (1500–3500 T/m) were optimized through controlled wrapping process to systematically investigate the structure-performance quantitative relationship. Results demonstrate that the 3000-T/m sample exhibits optimal sensitivity in the medium-to-high pressure range (1–25 N; <inline-formula> <tex-math>${S} =0.037$ </tex-math></inline-formula>/N at 25 N), whereas the 2500-T/m sample achieves significantly enhanced sensitivity in low-pressure regime (<1.0> <tex-math>${S} =9.37$ </tex-math></inline-formula>/N at 0.05 N), representing a nearly threefold improvement over its 1500 T/m counterpart (<1.0> <tex-math>${S} =3.12$ </tex-math></inline-formula>/N at 0.05 N). Moreover, the sensor demonstrates broad detection range (0.01–25 N), fast response time (0.4 s), and excellent cycling stability (no degradation after 7000 compression cycles). Through embroidery and weaving techniques, we fabricated finger-joint motion-monitoring gloves, plantar pressure-sensing insoles, and a <inline-formula> <tex-math>$16times 16$ </tex-math></inline-formula> sitting posture pressure matrix, validating its multiscale application potential in human motion capture and body pressure monitoring. This work provides a novel strategy for developing highly sensitive and customizable textile-based electronic sensors, advancing the practical implementation of wearable health monitoring technologies.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29744-29751"},"PeriodicalIF":4.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750946","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":"The FGC/CNTs-Based High-Sensitivity Temperature Sensor With Fast Response Time and Negative Temperature Coefficient","authors":"Miaoling Que;Jingwen Li;Hao Zhang;Zhiyong Zhao;Yunfei Sun","doi":"10.1109/JSEN.2025.3579021","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3579021","url":null,"abstract":"Temperature sensors play an essential and important role in the vigorous development of biomedical, smart home, and e-skin fields. It is meaningful to investigate a low-cost flexible temperature sensor that exhibits excellent performance. The introduction of flexible 3-D cloths can better fit home devices and bring the possibility of more application scenarios for temperature sensors. In this work, a sandwich structured sensor consisting of a polyimide (PI) film as a flexible substrate and encapsulation layer, and a fiberglass cloth modified with carbon nanotubes (FGC/CNTs) as a temperature sensitive layer exhibiting a negative temperature coefficient (NTC) is proposed. Results show that the flexible temperature sensor based on FGC/CNTs obtained a high sensitivity of −0.8122%<inline-formula> <tex-math>${}^{circ }text {C}^{-{1}}$ </tex-math></inline-formula> with excellent stability, fast response time (1.17 s), and a wide temperature sensing range (<inline-formula> <tex-math>$16~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$95~^{circ }$ </tex-math></inline-formula>C), due to the porosity, high temperature resistance, and corrosion resistance of fiberglass cloth. As a flexible device, it can be installed on a variety of daily necessities such as water cups, masks, and garbage bins for temperature monitoring. For example, the integration of temperature sensors into masks allows real-time and accurate detection of the experimenter’s breathing for identifying health conditions, demonstrating high potential applications in health monitoring. It can also be integrated into the bottom of the garbage bin and combined with a circuit board application to sense real-time temperature changes inside the garbage bin. When an abnormal temperature is detected, a fire alarm can be issued in a timely manner, providing a promising avenue for the subsequent development of wearable flexible temperature sensors for applications in human health.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29736-29743"},"PeriodicalIF":4.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750859","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 Novel Centrally Symmetric Spiral-Nested Piezoelectric Energy Harvesting System and Its Management Circuit","authors":"Yuxuan Liu;Debo Wang;Licheng Deng","doi":"10.1109/JSEN.2025.3578615","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578615","url":null,"abstract":"To realize high power density, low-frequency, multidirectional energy harvesting, and efficient energy storage management, a piezoelectric energy harvesting system comprising a centrally symmetric spiral-nested piezoelectric energy harvester (CSS-PEH) and an energy management circuit is proposed in this work. The harvester features a doubly clamped outer beam and a nested dual-spiral inner beam, while the management circuit integrates a self-powered parallel synchronized switch harvesting on inductor (SP-PSSHI) rectification circuit, BQ25570 IC, and supercapacitor. A three-degree-of-freedom (2-DOF) lumped parameter model and an electromechanical coupling model are established to analyze the frequency response and output characteristics. The measured results show that the CSS-PHE has resonant frequencies at 12.8 and 17.2 Hz, with corresponding open-circuit voltages of 73.5 and 38.7 V. Meanwhile, the SP-PSSHI rectified series circuit achieved maximum output powers of 6.23 mW with the normalized power density (NPD) of <inline-formula> <tex-math>$32.4~mu $ </tex-math></inline-formula>W<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>g<inline-formula> <tex-math>${}^{-{2}} cdot $ </tex-math></inline-formula>mm<inline-formula> <tex-math>${}^{-{3}}$ </tex-math></inline-formula>; the SP-PSSHI rectified parallel circuit delivered the maximum output power of 3.21 mW with the NPD of <inline-formula> <tex-math>$21.6~mu $ </tex-math></inline-formula>W<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>g<inline-formula> <tex-math>${}^{-{2}}cdot $ </tex-math></inline-formula>mm<inline-formula> <tex-math>${}^{-{3}}$ </tex-math></inline-formula>. The energy management circuit enables supercapacitor charging up to 4.05 V while maintaining stable 3.3-V dc output. Furthermore, the CSS-PEH demonstrates multidirectional energy harvesting capability. Therefore, this piezoelectric energy harvesting system provides stable power supply solutions for many fields, such as intelligent transportation energy harvesting, wireless sensor networks, and so on.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"26520-26529"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634656","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":"Cooperative Spectrum Sensing With DeGAN and KANCNN for Nonorthogonal Multiple Access","authors":"Mingqian Yan;Yonghua Wang;Quanbin Liang;Tinghui Xu","doi":"10.1109/JSEN.2025.3578359","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578359","url":null,"abstract":"Nonorthogonal multiple access (NOMA) technology achieves higher communication throughput compared with orthogonal multiple access, but it also introduces significant challenges for spectrum sensing (SS), particularly in accurately detecting channels occupied by multiple users in complex environments. To address these challenges and enhance SS capabilities in power-domain NOMA, this article proposes a novel deep learning-based algorithm that integrates a denoising generative adversarial network (DeGAN) for effective noise reduction. The DeGAN module employs a joint loss function to effectively suppress noise while preserving critical time–frequency features of the signals. Subsequently, the power spectral density of the denoised signals is extracted and utilized as a feature for sample classification through a model that integrates the Kolmogorov–Arnold networks–convolutional neural network (KANCNN). Comparative results demonstrate that the DeGAN–KANCNN algorithm surpasses other methods in detection accuracy and interference resistance in challenging environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"27265-27277"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634740","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":"Flow Characterization in Inclined Intermittent Flow Using Improved U-Net and Particle Image Velocimetry","authors":"Ting Xue;Zeyang Hao;Yan Wu","doi":"10.1109/JSEN.2025.3578715","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578715","url":null,"abstract":"Gas-liquid intermittent flow is of great engineering significance for the design and optimization of pipeline systems in complex terrains, while current research on the flow characteristics influenced by pipe inclination angle remains insufficient. In this study, the flow characteristics of intermittent flow in horizontal, 5° and 10° inclined pipelines are systematically analyzed by combining the improved deep learning model with particle image velocimetry (PIV) technology. First, the improved U-Net model integrating the convolutional block attention module (CBAM) is employed to achieve high-precision segmentation of the gas-liquid phase. Experimental results show that the improved model achieves 98.83% pixel accuracy (PA) and 97.01% mean intersection over union (MIoU) in phase segmentation tasks, which surpasses benchmark models, including DeepLabV3 and HRNet. By comparing the three inclined configurations, the pipe inclination angle is found to significantly alter the flow structure by increasing the axial gravitational component, which manifests in reduced length of elongated bubbles, decreased thickness of liquid film, and enhanced asymmetry in flow velocity distribution. Furthermore, the increase of the inclination angle will trigger the flow regime transition to unstable slug flow. The flow pattern transition boundary is established based on the mixed Froude number (Fr), revealing that the critical Fr value for the transition from slug flow to plug flow in the 10° inclined pipe decreased by 22% compared to the horizontal pipe. Research results provide essential parameter references for flow stability prediction and numerical simulation in pipeline design across complex terrains.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"27278-27287"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634673","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}