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{"title":"Hardware-Efficient Noise-Level Estimation for Image Denoising With FrWF and Polynomial Regression-Based Edge Detection","authors":"Anuja George;E. P. Jayakumar","doi":"10.1109/JSEN.2025.3581513","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581513","url":null,"abstract":"Image noise estimation is vital in noise removal in biomedical imaging and computer vision applications. A precise calculation of the noise standard deviation is required for the image-denoising algorithms. An efficient noise estimation method is proposed using fractional wavelet filter (FrWF) and polynomial regression-based edge detection. The edge detection suggested in this study employs an adaptive edge threshold estimation based on polynomial regression and has lower hardware demands than the existing Sobel edge detection with Otsu thresholding. The suggested noise estimation technique performs competitively in terms of noise estimation accuracy when compared to earlier sophisticated algorithms. A very large-scale integration (VLSI) architecture design for the suggested noise estimation technique is also provided. The proposed design is modeled in Verilog hardware description language (HDL), simulated using Vivado 2019.1, and synthesized for TSMC 90 nm CMOS technology by Cadence Genus Synthesis Solution. The implementation of the proposed noise estimation algorithm demands an area of <inline-formula> <tex-math>$210354.62~ mu text {m}^{{2}}$ </tex-math></inline-formula>, consumes 5.75 mW power, and has an operating frequency of 120 MHz. The suggested design is accurate and hardware-efficient which is the key highlight of this work.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29872-29879"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750875","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 Flexible Forceps With FBG-Based Force-Sensing and Feedback for Endoscopic Surgery","authors":"Jinxiu Guo;Baojun Chen;Zheng Zhang;Chengyu Zhang;Chi Zhang;Siyang Zuo","doi":"10.1109/JSEN.2025.3581568","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581568","url":null,"abstract":"Endoscopic submucosal dissection (ESD) is an effective treatment for early gastrointestinal (GI) tract cancers. However, existing endoscopic instruments lack force-sensing and feedback control capability, potentially leading to serious complications. In this work, we developed novel four degrees of freedom (DOF) flexible forceps capable of sensing both clamping and dragging forces. The compact and conformal design integrates two fiber Bragg grating (FBG)-based clamping force-sensing units (CFSUs) and one FBG-based dragging force-sensing unit (DFSU). These units achieve high resolutions of 0.76 mN (clamping) and 5.88 mN (dragging), excellent dynamic force tracking precision and temperature compensation capability. By combining the flexible forceps with a haptic device, we implemented a proportional leader-follower force feedback control method, whose feasibility was successfully verified through ex vivo experiments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28427-28434"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756770","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":"Research on Frequency-Tunable Microwave Resonant Cavity Sensor to Overcome the Influence of Salinity in Ultralow Water Content Detection","authors":"Ying Xu;Zibo Sun;Chao Yuan;Rongji Zuo;Haixin Mo;Yumeng Zhang;Cenwei Sun","doi":"10.1109/JSEN.2025.3581783","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581783","url":null,"abstract":"Accurate measurement of ultralow water volume fraction (WVF) (ranging from 0% to 3%) is critically important during the production process and trade settlement at crude oil joint stations. Traditional techniques utilizing electrical, microwave, and radiation measurements are invariably affected by salinity. This study employs microwave resonant cavity technology to target ultralow WVF. Further design of the resonant cavity sensor structure is carried out, and a salinity compensation method based on a mechanical tuner is proposed. Theoretical analysis, simulations, and experimental results show that the tuner inserted into the microwave resonant cavity can effectively influence the resonance mode frequency, with simulation and experimental results showing good agreement. A prototype based on the design principle was developed, and by adjusting the insertion depth of the tuner, the influence of salinity within the 30 parts per thousand (ppt) range was mitigated. The measurement deviation of WVF is reduced from 2.5‰ to 0.6‰. A comparison between experimental and simulation results reveals a systematic deviation between the tuner depth h and the frequency shift. After calibration, the deviation is reduced to only ±0.2%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28341-28348"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758224","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":"Highly Sensitive and Selective Room-Temperature NO2 Gas Sensor With Ce2Sn2O7/g-C3N4 Nanocomposite","authors":"Mathankumar Ganesan;Jin Li;Fei Wang","doi":"10.1109/JSEN.2025.3581542","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581542","url":null,"abstract":"In this study, we have fabricated a highly sensitive and selective room-temperature nitrogen dioxide (NO2) gas sensor based on a novel composite (Ce2Sn2O7 /g-C3N4) prepared by a facile hydrothermal method. The fabricated Ceg1 sensor exhibits outstanding sensing performance at room temperature (<inline-formula> <tex-math>$28~^{circ }$ </tex-math></inline-formula>C) and highly responds (<inline-formula> <tex-math>${R} =6.57$ </tex-math></inline-formula>) toward 10 ppm of NO2. The fabricated Ceg1 sensor displays a superior selectivity toward NO2 gas molecules compared to various gas molecules and reflects the quick response time of 7 s toward the 10 ppm of NO2 gas molecules. Moreover, the Ceg1 sensor preserved 89% of its response for 60 days, exhibiting outstanding stability. Additionally, the Ceg1 sensor showed the ultralow detection limit of 48 ppt. The exceptional performance is attributed to the novel interface between Ce2Sn2O7 and g-C3N4, combined with the novel combination’s high specific surface area and oxygen vacancies. This novel method not only boosts the performance of NO2 detection but also significantly contributes to the advancement of gas sensors critical for environmental monitoring and protecting human health.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"27997-28004"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758365","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":"Progressive Gaussian Filtering With Classification for Nonlinear Systems With Composite Noise","authors":"Wen-An Zhang;Weiyi Li;Xusheng Yang;Ya Zhang;Shaohua Tian","doi":"10.1109/JSEN.2025.3581597","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581597","url":null,"abstract":"In this article, the nonlinear filtering problem of composite noise is studied, and a progressive Gaussian filtering method with classification is proposed to improve the filtering robustness and accuracy. First, for time-varying and different statistical properties of the noise caused by environmental occlusion, etc., the hypothesis test on residual is used to classify the measurements based on the confidence levels. Second, the pseudo-time is introduced to establish a progressive process from prior to posterior estimation; thus, the problem of measurement uncertainty compensation is transformed into that of pseudo-time duration control. Specifically, based on the measurement classification, the uncertainties over the low-confidence measurements are compensated by using high-confidence measurements as a third-party reference to adaptively adjust the pseudo-time duration. Finally, taking the GNSS/INS tightly coupled positioning scenario as an example, experiments are designed to verify the effectiveness and superiority of the proposed method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29553-29564"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758329","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":"GSD-YOLO: A Gear Surface Defects Detection Method Using Adaptive Multiscale Fusion and Hybrid Feature Fusion","authors":"Shihua Zhou;Zichun Zhou;Kaibo Ji;Yiyan Wang;Xin Zhou;Tianzhuang Yu;Zhaohui Ren","doi":"10.1109/JSEN.2025.3581717","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581717","url":null,"abstract":"Gear surface is the main working interface of the gear, which directly influences the safety and lifespan of mechanical equipment with different gear surface defects. Due to the large-scale variations, diversified types, multiple faults overlapping, noise interference, and low contrast between background and defects, the GSD detection is prone to occur false detection or missed detection. To address the issues and precisely identify the gear surface defect, a novel GSD-you only look once (YOLO) network based on YOLOv5 is proposed. First, an adaptive multiscale fusion (AMF) module is constructed and used to displace the C3 module in the neck structure, which can enhance the feature extraction capability of the neck for multiscale and multitype GSD. Then, the directional feature extractor (DFE) module is integrated into hybrid feature fusion (HFF) module. Afterward, the HFF module is added before the detect layer and the recognition ability for low contrast and overlapping defects is improved. Finally, the random noise is introduced in the data augmentation process to reinforce antinoise performance. Experimental analysis shows improvements in mAP by 2.3% and 3.0% on NEU-GSD and RSDDs datasets, respectively. Meanwhile, the improved GSD-YOLO shows stronger robustness and generalization ability when dealing with complex defects, and the mAP values reach 96.3% and 82.1% on the two datasets, which are better than other advanced models.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"30020-30033"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758412","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":"AlScN Multilayer SAW Resonators Achieving K2eff > 4% in Sezawa Mode With Au Bonding Layer","authors":"Jiazhe Zhang;Shaocheng Wu;Fei Lu;Zhenglin Yang;Yuhang Dou;Daquan Yu;Baoping Zhang;Rongbin Xu","doi":"10.1109/JSEN.2025.3581581","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581581","url":null,"abstract":"This work proposed AlScN-based surface acoustic wave (SAW) piezoelectric resonators using AlScN/Mo/Au/SiO2/Si multilayer structure. The SAW mode characteristics were analyzed by finite element modeling (FEM) simulation, and the key role of the Au layer in the Sezawa mode excitation frequency and coupling coefficient was verified. By using bonding and laser lift-off process, AlScN-based multilayer SAW resonators were fabricated. Due to the excellent crystallinity of Al0.74Sc0.26N film (X-ray diffraction (XRD) full width at half maximum (FWHM) <1°)> <tex-math>$K^{{2}}_{text {eff}}$ </tex-math></inline-formula>) of more than 4% was achieved on Si substrate, revealing the potential of such a multilayer film structure to sensing and conversion.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28005-28011"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758421","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":"Research on Low-Light Environment SLAM System via Retinex Enhancement and Deep Feature Optimization","authors":"Kuosheng Jiang;Chengbing Zhu;Jinbao Yang","doi":"10.1109/JSEN.2025.3581532","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581532","url":null,"abstract":"With the rapid advancements in autonomous driving, robot navigation, and related fields, Visual simultaneous localization and mapping (Visual SLAM) technology plays a vital role in real-time positioning and map creation. However, in low-light environments such as coal mines, tunnels, and underground parking lots, challenges like poor image quality, sparse feature points, and color distortion significantly hinder extracting and matching these feature points in Visual SLAM. As a result, the positioning performance in such environments declines sharply. To this end, this article proposes an improved SLAM method that integrates deep learning techniques, specifically targeting the issues of feature point extraction and matching in low-light environments. The core idea of this method is to introduce Retinexformer, which is based on the Retinex principle, at the front end to enhance low-light images based on the ORB-SLAM3 algorithm framework. The proposed approach improves image clarity and contrast by preprocessing the input images, enhancing the SLAM system’s perception in low-light conditions. Furthermore, to address the issue of sparse feature points in low-light environments, this article proposes an efficient feature extraction and matching module, which further improves the map construction and positioning accuracy of the SLAM system while improving computational efficiency. We conducted extensive experiments on public datasets and real low-light scenarios. The results demonstrate that the proposed algorithm exhibits greater robustness and higher accuracy in low-light environments than traditional SLAM algorithms. The proposed algorithm effectively improves SLAM systems’ perception and positioning performance in low-light conditions by enhancing image quality and strengthening feature point processing capabilities.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29992-30004"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758352","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 Nondestructive Liquid-Level Sensor Based on Spoof Surface Plasmon Polaritons Controlled by Liquid Metal Switches","authors":"Jieping Wu;Weilai Wang;Maode Liu;Shuangxi Xue;Guangming Yang;Xiaoqing Yang","doi":"10.1109/JSEN.2025.3581232","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3581232","url":null,"abstract":"This article proposes a nondestructive multichannel sensor for liquid-level detection based on spoof surface plasmon polaritons (SSPPs). A mathematical model for liquid-level sensing is established by leveraging the dispersion relationship between the permittivity and the phase shift constant. Then, liquid metal switches are integrated into the SSPP channels to dynamically control signal propagation, thereby enabling selective activation of each channel for level detection. The proposed detection theory and dynamic control method are validated through simulations. Subsequently, the designed sensors are fabricated and measured. The results demonstrate a linear relationship between the liquid level and the phase offset. The sensor is capable of penetrating nonmetallic containers for liquid-level detection, with a measurement range determined solely by the length of the component. It achieves a resolution of 0.01 mm and a detection accuracy of less than 1.28 mm. Moreover, the multichannel SSPP design enables the acquisition of more detailed level information, supporting dynamic liquid-level monitoring.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29841-29850"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751023","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":"FMCW Radar-Based Human Activity Recognition Based on Higher Order Synchrosqueezing Transform","authors":"Krishna Kumar Mishra;Ram Bilas Pachori","doi":"10.1109/JSEN.2025.3579423","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3579423","url":null,"abstract":"Human activity recognition (HAR) plays a fundamental role in various health care and smart home automation. The occurrence of falls is a key focus in HAR, due to its implications in safety-critical applications, such as elderly monitoring and assisted living. In previous studies, radar-based HAR utilizes discrete Fourier transform (DFT), short-time Fourier transform (STFT), and wavelet transform-based approaches, but they fail due to poor resolutions and fixed basis function to distinguish closely spaced frequency components in the radar signals. To mitigate these limitations, we present a new method for HAR based on the higher order synchrosqueezing transform and deep learning (DL) classifiers from radar return signals. The frequency modulated continuous wave (FMCW) radar return signal captures the micro- and macro-motion of human activity. To analyze such signals, the fourth-order Fourier synchrosqueezing transform (FSST4) technique plays a vital role due to its impressive time–frequency resolutions in the resultant time–frequency representation (TFR). The DL techniques (MobileNetV2, GoogLeNet, AlexNet, VGG16, and VGG19) are used to classify the FSST4-based TFRs into various activity classes. The method based on FSST4 and AlexNet achieved the highest accuracy of 99.40% among the studied classifiers. A comparative study is also performed to study the effectiveness of FSST4-based TFRs as compared with STFT, continuous wavelet transform (CWT), Fourier synchrosqueezing transform (FSST), second-order Fourier synchrosqueezing transform (FSST2), third-order Fourier synchrosqueezing transform (FSST3), and FSST4-based TFRs in the proposed method. The proposed method with FSST4-based TFR provided superior performance as compared with other TFR-based proposed methods. The comparison of computational complexity analysis of FSST4 with other methods, such as STFT, CWT, FSST, FSST2, and FSST3, is also studied in this work. The CWT is found to be the fastest among studied methods for TFR computation. The FSST4 method required a similar computation time compared with STFT, FSST, FSST2, and FSST3 for obtaining TFRs providing relatively better performance.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28934-28941"},"PeriodicalIF":4.3,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763580","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}