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{"title":"Stochastic Templates and Noise Dynamics in Memristor Cellular Nonlinear Networks","authors":"Dimitrios Prousalis;Vasileios Ntinas;Christoforos Theodorou;Ioannis Messaris;Ahmet Samil Demirkol;Alon Ascoli;Ronald Tetzlaff","doi":"10.1109/TNANO.2025.3565887","DOIUrl":"https://doi.org/10.1109/TNANO.2025.3565887","url":null,"abstract":"Noise is a pervasive aspect that impacts various systems and environments, from mobile radio channels to biological systems. Within the framework of complex networks, noise poses significant challenges for functionality and performance. In this paper, we investigate the dynamics of a well-known type of locally-coupled computing networks, Memristor Cellular Nonlinear Networks (M-CNNs), in the presence of noise at their interconnection weights, introducing the concept of stochastic weights. In particular, we analyze the effect of noise originating from the synaptic memristors by incorporating both deterministic and stochastic components into synaptic weights, investigating how device-to-device variability and noise affect network performance. Based on the well-established theory of CNNs, we are extending the stability criteria to incorporate synaptic memristor non-idealities and we provide a theoretical framework to analyze their effect on system's performance. In this work, we employ the physics-based Jülich Aachen Resistive Switching Tools (JART) model to study Valence Change Memory (VCM) devices as synapses within our theoretical framework. We investigate the impact of device variability and noise, utilizing statistical properties derived from experimental data reported in the literature. We demonstrate the efficacy of noisy M-CNNs in performing the edge detection task, an example of fundamental image processing applications.","PeriodicalId":449,"journal":{"name":"IEEE Transactions on Nanotechnology","volume":"24 ","pages":"282-292"},"PeriodicalIF":2.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Step Strategy for Pattern Recognition and Rotation Angle Estimation of Rectangular Workpieces","authors":"Xiaoxian Wang;Yinan Sun;Anglong Li;Jingfeng Lu;Juncai Song;Siliang Lu","doi":"10.1109/JSEN.2025.3564001","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3564001","url":null,"abstract":"The burgeoning field of automated assembly is undergoing rapid evolution, thanks to the recent strides in deep learning and computer vision technologies. However, the journey is marred by significant challenges, particularly inaccurate classification precision and suboptimal positioning accuracy, which stifles technological progression. To address these challenges, this study proposes a new Swin Transformer and ORB (STO) algorithm, aimed at improving the classification, positioning, and rotation accuracy of key components, especially rectangular objects, in automated assembly lines. The STO algorithm consists of three main components: a Swin Transformer-based object classification system, a positioning model for rectangular objects, and a model for calculating rotation angles. The positioning model uses the techniques of threshold processing and contour detection to locate rectangular objects effectively. Meanwhile, the rotation angle calculation model employs the oriented FAST and rotated BRIEF(ORB) algorithm for feature extraction and matching, ensuring precise determination of the required rotation angles. This study sets up an experimental apparatus including a camera, a robotic arm, and randomly placed rectangular workpieces. The randomly placed rectangular workpieces are regarded as rectangular workpieces that need to be assembled. Results demonstrate that the STO algorithm excels in object recognition and angle determination, particularly showing high precision in angle calculation, with a mean absolute error (MAE) of 0.10°. In summary, the proposed method improves the accuracies of rotation angle estimation and pattern recognition of the workpieces, thereby showing potential applications in the industrial assembly process. The STO method principle also shows potentials in recognizing irregular workpieces in various industrial scenarios.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20272-20284"},"PeriodicalIF":4.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205861","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":"NeurINS: Neural Inertial Navigation System for Consistent and Drift-Free State Estimation","authors":"Meng Liu;Yan Li;Liang Xie;Wei Wang;Zhongchen Shi;Wei Chen;Erwei Yin","doi":"10.1109/JSEN.2025.3563478","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563478","url":null,"abstract":"The data-driven inertial navigation system (INS) utilizing a low-cost inertial measurement unit (IMU) is remarkably autonomous and impervious to external signals, demonstrating considerable potential in environments, such as indoors and underground. However, existing INS studies still face two challenges. First, they cannot guarantee global consistency, that is, the spatial correspondence between a personal IMU and the global world. Second, they are unable to support robust long-term state estimation since their open-loop integration may incur pose drift over time. To address the two challenges, we propose a neural network-aided INS (NeurINS) that leverages solely an IMU for consistent and drift-free state estimation. For the first challenge, we propose a consistency regression network (CR-Net) to extract the global consistency implicit in IMU measurements and predict consistency vectors. These vectors serve as global constraints for the subsequent joint state estimation, enabling the recovery of spatial correspondence between the IMU and world frames. For the second challenge, a novel neural-inertial fusion method is proposed to jointly estimate the six-degree-of-freedom (6-DoF) pose, velocity, and IMU biases, where a state-space description is modeled, and a resilient filter is utilized as the estimator. Considering the accuracy and smoothness of localization over short durations, we propose a velocity regression network (VR-Net) to provide relative motion constraints. Experimental evaluations on the IDOL dataset demonstrate that NeurINS achieves consistent and drift-free state estimation using only a consumer-grade IMU. Specifically, the root mean squared errors (RMSEs) of NeurINS are 0.95 m for absolute trajectory error (ATE) and 3.44° for absolute orientation error (AOE), representing reductions of 71% and 54%, respectively, compared with other methods. Trials on our self-collected dataset further prove the superior performance of NeurINS.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20224-20237"},"PeriodicalIF":4.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205851","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":"RF Attenuation Measurements in Compacted Bentonite Buffer Materials Using Software-Defined Radios","authors":"Jiwook Choi;Yonghyeon Lee;Jin-Seop Kim;Chang-Ho Hong","doi":"10.1109/JSEN.2025.3563804","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563804","url":null,"abstract":"Compacted bentonite buffers are crucial components in engineered barrier systems (EBSs) for high-level radioactive waste (HLW) disposal. However, their physical properties significantly attenuate radio frequency (RF) signals, complicating wireless sensor implementation. This article experimentally investigates the RF attenuation characteristics of various compacted bentonite buffers using software-defined radios (SDRs). Both powder- and granule-type bentonite blocks were fabricated and evaluated under different compaction pressures and water contents. For measurements, the SDR transmits frequency-offset single-tone signals with pulse shaping, while changing the transmit power and carrier frequency. Experimental results indicate that granule-type bentonite exhibits greater RF signal attenuation than powder-type bentonite due to its higher dry density. Furthermore, even under identical dry density conditions, granule samples are more sensitive to water content variations than powder samples. Additionally, engineering-scale tests show that each additional 25 cm of buffer thickness increases signal attenuation by about 12 dB at a dry density of 1.6 g/cm3. These findings provide valuable insights for designing wireless monitoring sensors in HLW disposal systems, highlighting the impact of bentonite type, density, moisture content, and thickness on RF signal propagation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20829-20841"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196667","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":"Enhancing SEEG-Based Speech Decoding via Convolutional Encoder-Decoder and Scale-Recursive Reconstructor","authors":"Vincent Yuanyu He;Aiping Liu;Shengcai Duan;Yikai Gao;Ruobing Qian;Xun Chen","doi":"10.1109/JSEN.2025.3563587","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563587","url":null,"abstract":"A brain-computer interface (BCI) that decodes speech directly from neural activity provides a rapid and natural means of communication for individuals with speech impairments or aphasia. Recent advances in deep learning have led to several studies demonstrating promising outcomes using electrocorticography (ECoG) placed on cortical surfaces. In contrast, stereo electroencephalography (SEEG) captures neural signals from multiple brain regions, including the cortex and subcortex. These signals, encompassing rich information from deeper brain structures, have significant potential to enhance the characterization of speech generation processes and improve decoding performance. However, effective SEEG-based decoding schemes remain limited. Existing deep learning methods often struggle with overfitting due to insufficient data. In addition, the reconstructed speech tends to be blurry and detail-deficient, indicating an urgent need for more refined SEEG modeling. To address these issues, a convolutional encoder-decoder with scale-recursive reconstructor (ConvED-SR) is proposed for SEEG speech decoding. ConvED-SR first extracts multiscale speech-related features from SEEG signal using a convolutional encoder-decoder architecture. This creates a compact and efficient latent feature space with reduced parameters, thus mitigating overfitting. Furthermore, these multiscale features, effectively characterizing the intricate relationships between deeper neural signals and speech, are used to generate a refined Mel-spectrogram by a scale-recursive reconstructor. The reconstructor initially models low-frequency information, gradually interacts with high-frequency information, and ultimately refines a coarse Mel-spectrogram into a detailed final one. Finally, a HiFi-GAN vocoder converts the spectrogram into speech. Comprehensive experimental results on the SingleWordProductionDutch-iBIDS dataset demonstrate that ConvED-SR achieves superior performance, providing a promising solution for SEEG-based speech decoding.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20250-20262"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205950","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":"Retrieval of Gas Temperature and Pressure in Brillouin Lidar Based on Reversal Modeling Method","authors":"Peng Zhang","doi":"10.1109/JSEN.2025.3563720","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563720","url":null,"abstract":"In order to address the challenge of improving the retrieval accuracy of Rayleigh-Brillouin (RB) lidar using conventional retrieval method, a novel method called reversal modeling is proposed in this work. This method establishes a retrieval model using two sets of RB spectral (RBS) linewidths that have a reversal relationship. Theoretical analysis shows that the temperature fit errors of novel method within 1 and 2 K account for 95.3% and 100%, respectively, while the pressure fit errors of novel method within 0.025 bar and 0.05 bar account for 98.1% and 100%, respectively. Furthermore, compared to dual-parameter model presented by Zhang in 2020, the novel model is of similar temperature theoretical errors but smaller pressure theoretical errors, as well as smaller temperature and pressure fit errors under various wavelength conditions, particularly achieving higher retrieval accuracy at shorter wavelengths. Finally, experimental results demonstrate that the mean relative temperature and pressure errors of the novel method are 0.22% and 4.093%, respectively, which are more than 50% lower than those of the conventional method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20263-20271"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206151","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":"Room Temperature Negative Differential Resistance in Gate-All-Around Field-Effect Transistors With 1D Active Channels","authors":"Amit Verma;Reza Nekovei;Daryoush Shiri","doi":"10.1109/TNANO.2025.3565276","DOIUrl":"https://doi.org/10.1109/TNANO.2025.3565276","url":null,"abstract":"We report on the presence of a Negative Differential Resistance (NDR) in a Gate-All-Around Field Effect Transistor (GAAFET) with 1D nanowires or nanotubes as the active conducting channel. Here, the drain current is seen to decrease sharply at relatively higher gate voltages. The onset of NDR is tunable with device topology. The NDR mechanism in this work is due to the applied gate voltage, not the drain-source voltage, a feature which promises low-voltage application of this effect. The results are based on a self-consistent ensemble Monte Carlo charge-carrier transport model with an electrostatic solver that solves Gauss's law in integral form.","PeriodicalId":449,"journal":{"name":"IEEE Transactions on Nanotechnology","volume":"24 ","pages":"260-263"},"PeriodicalIF":2.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Sensor Applications of Microstructured Optical Fibers: A Review","authors":"Eduardo Urrutia;Igor Ayesta;Mikel Azkune;María Asunción Illarramendi;Joseba Zubia","doi":"10.1109/JSEN.2025.3558308","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558308","url":null,"abstract":"The collection and interpretation of environmental data in a reliable and secure manner is of undeniable importance for the enhancement of healthcare quality and the mitigation of ecological risks associated with human activities. In pursuit of this objective, optical fibers have been utilized for several decades as a means of providing more flexible, compact, and cost-effective sensing platforms. Furthermore, the advent of technologies, such as the laser, improved fabrication processes, and 3-D printing, has made it possible to engrave microstructures into optical fiber cores, resulting in a significant enhancement in sensitivity without compromising portability and compactness. The development of microstructured optical fibers (MOFs) has transformed the domain of optical sensing, as their distinctive capacity to confine and manipulate light surpasses that of conventional fibers. Their diverse structural configurations, encompassing fiber Bragg gratings (FBGs), photonic crystal fibers (PCFs), and tapered fibers, offer enhanced sensitivity and adaptability, rendering them invaluable in diverse applications. This review offers a comprehensive overview of recent advances in MOF technologies, emphasizing significant innovations in fiber design and fabrication and their influence on sensor performance over the past year. It is intended to serve as a comprehensive introductory resource for researchers and engineers, delineating the latest trends, technological advantages, and future directions in the rapidly evolving field of MOF-based sensors. Moreover, particular emphasis is placed on specific MOF types, including hollow-core and anti-resonant fibers (ARFs), which have demonstrated considerable potential in environmental monitoring, biomedical sensing, and industrial applications. Additionally, this review encompasses the advancement of emerging structures, such as D-shaped and multicore fibers, which continue to challenge the limits of fiber-based sensors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"18584-18607"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10980192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196799","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":"Mechanism-Constrained Multistage Recursive Soft Sensor Framework for Slab Temperature Prediction in Reheating Furnace","authors":"Yinghua Yang;Yu Zhou;Dandan Yao;Xiaozhi Liu","doi":"10.1109/JSEN.2025.3563586","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563586","url":null,"abstract":"Slab temperatures are difficult to measure directly in real time during the heating process in the reheating furnace. This article analyzes the slab heating process as a multistage manufacturing system (MMS) and proposes a soft sensor framework named mechanism-constrained multistage recursive network (MC-MRN) to predict slab temperature. The proposed method first uses the mechanism model to generate label data for each stage as the pretraining basis and introduces a new prediction regularization term in its loss function using the mechanism information to guide and constrain the feature extraction process so that the extracted features more comprehensively reflect the original data and its stage information. Furthermore, based on the physical relationship between stages, we connect the pretrained models of each stage in series and input the features containing the production information from all stages into the prediction network for fine-tuning, thereby constructing an overall soft sensor model framework. This design ensures that the framework structure aligns with the physical structure of the reheating furnace, and the constraints of the mechanism model enhance the interpretability and reliability of the framework, ensuring that its predictions remain within a reasonable range consistent with the laws of physics. The experimental results show that the MC-MRN soft sensor framework, after fine-tuning, demonstrates high accuracy in slab temperature prediction.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"20970-20981"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196630","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 All-Solid-State Levocetirizine-Selective Potentiometric Microsensor","authors":"Nurşen Dere","doi":"10.1109/JSEN.2025.3563624","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3563624","url":null,"abstract":"In this study, a novel all-solid-state potentiometric microsensor was developed for the selective and sensitive determination of levocetirizine dihydrochloride (LEV.2HCl) in pharmaceutical drug samples. For this purpose; levocetirizine-tetraphenylborate (LEV-TPB) ion-pair was synthesized using LEV.2HCl and sodium tetraphenylborate (NaTPB) and the microsensor was developed by using this ion-pair as an ionophore in the structure of the microsensor. The optimum membrane composition of the levocetirizine-selective (LEV-selective) microsensor was determined and the potentiometric performance characteristics were investigated. The detection limit of the proposed microsensor was calculated as <inline-formula> <tex-math>$3.5times 10^{-{7}}$ </tex-math></inline-formula> <inline-formula> <tex-math>$mathrm{mol.L}^{-{1}}$ </tex-math></inline-formula>. The response time of the microsensor was significantly short (<inline-formula> <tex-math>$le 10$ </tex-math></inline-formula> s). The microsensor showed a super-Nernstian response with a slope of <inline-formula> <tex-math>$59.9pm 0.6$ </tex-math></inline-formula> mV (<inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula>: 0.9991) over a wide concentration range of <inline-formula> <tex-math>$10^{-{6}}- 10^{-{2}}$ </tex-math></inline-formula> <inline-formula> <tex-math>$mathrm{mol.L}^{-{1}}$ </tex-math></inline-formula> for LEV.2HCl solutions over seven weeks and no significant drift in potentials. The microsensor was determined to have optimum performance in the pH range of 4.0–8.0. The microsensor was successfully used to determine levocetirizine in pharmaceutical samples. The results were statistically compared with the UV-Vis spectroscopy method. The obtained potentiometric results were in good harmony with the results obtained by the UV-Vis spectroscopy method at a confidence level of 95%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"18750-18758"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196642","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}