IEEE Open Journal of Instrumentation and Measurement最新文献

{"title":"Measurement Uncertainty and Deep Learning: A Metrology-Aware Framework for Load Forecasting","authors":"V. Negri;S. Mari;F. Ciancetta;A. Mingotti;R. Tinarelli;L. Peretto","doi":"10.1109/OJIM.2026.3711302","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3711302","url":null,"abstract":"Accurate load forecasting is essential for power system operation, yet most deep learning (DL) pipelines neglect the measurement uncertainty affecting sensor-acquired input data. This article proposes a metrology-aware, data-centric, and architecture-agnostic framework that incorporates instrumental uncertainty into DL workflows without modifying the predictive model. Input uncertainty is modeled during training through guide to the expression of uncertainty in measurement (GUM)-consistent data augmentation (DA) and during inference through repeated perturbation of the test samples. The framework is validated on public Ausgrid substation data using a long short-term memory (LSTM) network for one-day-ahead and one-week-ahead forecasting. Representative results show that uncertainty-aware DA reduces the one-week-ahead mean absolute percentage error (MAPE) from 0.12 to 0.09 in the single-substation forecasting scenario and the one-week-ahead mean absolute error (MAE) from 1.80 to 1.59 MW in the cross-substation generalization scenario, corresponding to relative reductions of approximately 25% and 12%, respectively. The analysis also considers robustness under perturbed inputs, cross-substation generalization, computational cost, statistical significance, and comparisons with model-centric baselines based on deep ensembles and Monte Carlo Dropout. Repeated uncertainty-aware testing shows very small variability under realistic input perturbations, with standard deviations lower than <inline-formula> <tex-math>$10^{-3}$ </tex-math></inline-formula> MW for MAE and root mean square error (RMSE) and lower than <inline-formula> <tex-math>$10^{-4}$ </tex-math></inline-formula> for MAPE. The corresponding empirical interval widths remain limited, with mean prediction interval width (MPIW) values below 0.06 MW in all the considered cases. Conversely, model-centric baselines exhibit larger variability, up to 0.18 MW for MAE, 0.22 MW for RMSE, and 0.02 for MAPE in the ensemble configuration, confirming that model-related uncertainty dominates measurement-induced uncertainty in the considered case study. Overall, the proposed framework bridges metrological principles and DL-based forecasting, enabling more robust and traceable predictions for measurement-intensive power applications.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"2500408-2500408"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11599816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping and Tracking Conductivity in Memristive Nanowire Networks With Electrical Resistance Tomography","authors":"Alessandro Cultrera;Gianluca Milano;Carlo Ricciardi;Luca Callegaro","doi":"10.1109/OJIM.2026.3690870","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3690870","url":null,"abstract":"In recent years, electrical resistance tomography (ERT) has been successfully applied to map the conductivity of thin-film materials and graphene. Beyond the mapping of static conductivity distribution, ERT is proposed as a new valuable tool for the characterization of memristive materials that present a dynamic behavior, where the conductivity evolves over time depending on the external stimulation. In this work, we show that ERT can provide insight into both the conductivity distribution and its time evolution in memristive metallic nanowire (NW) networks, enabling visualization of induced conductive traces in samples potentiated by means of the ERT setup itself.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"6000109-6000109"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11510303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensor-System Characterization of CNN-KAN Person Detection on Dual-Tier IoT Platforms With Uncertainty Quantification","authors":"Daniele Faggi;Oleksandr Kuznetsov;Emanuele Frontoni","doi":"10.1109/OJIM.2026.3712887","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3712887","url":null,"abstract":"Qualifying the metrological properties of an AI-based Internet of Things (IoT) sensor system requires explicit identification of the measurands, characterization of uncertainty sources following established metrological principles, and on-device validation against a trusted desktop reference. Hybrid convolutional neural network–Kolmogorov–Arnold network (CNN-KAN) models can be deployed on microcontrollers after operator decomposition, yet the impact of sensor-system factors on end-to-end measurement uncertainty remains uncharacterised. This article presents a systematic evaluation of a dual-tier IoT architecture (XIAO ESP32S3 Sense edge node and Raspberry Pi 3 gateway) with explicit uncertainty quantification for all measurements. Four measurands are defined and characterized: 1) on-device inference latency [s]; 2) on-device classification accuracy [%]; 3) CPU junction temperature [°C]; and 4) desktop-to-device accuracy gap [pp]. We quantify four system-level effects neglected by model-centric studies: 1) TensorFlow lite (TFLite) Microruntime selection introduces a <inline-formula> <tex-math>$4.3times $ </tex-math></inline-formula> latency variation (±3% relative uncertainty), a pure measurement variability component; 2) a per-channel versus per-tensor quantization mismatch causes a silent 1.8 pp accuracy drop (95% CI ±0.2 pp), a systematic bias that was detected and corrected; 3) compiler optimization flags yield a deterministic <inline-formula> <tex-math>$1.82times $ </tex-math></inline-formula> speedup without accuracy change; and 4) sustained-inference thermal throttling adds 7%–9% latency [±1.5% interquartile range (IQR)], a measurement drift effect. We explicitly distinguish between systematic errors, random variability, and drift. All experimental procedures, repeatability metrics, and source code are publicly available to ensure traceability and reproducibility. These findings demonstrate that runtime, toolchain, and quantization handling are first-order parameters that dominate model-level design choices and must be accompanied by measurement uncertainty budgets in systematic bias-aware AI deployments.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"2500507-2500507"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11606496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Precision of Impedance Argument Estimation in the Presence of Additive Noise","authors":"Mariana Lopes Teixeira;Francisco André Corrêa Alegria","doi":"10.1109/OJIM.2026.3709373","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3709373","url":null,"abstract":"Estimating the argument of an electrical impedance is an important task in many engineering fields. Modern systems usually employ data acquisition systems to sample and digitize the voltage and current signals obtained by stimulating the unknown impedance with a sinusoidal signal. The presence of nonideal phenomena in signal generation or signal measurement inevitably leads to an increased uncertainty in impedance argument estimation. This work focuses on the effect of additive noise and proposes an analytical expression that can be easily used to compute the impedance argument standard deviation as a function of additive noise precision, number of samples, stimulus signal amplitude, current sampling resistance value, and nominal impedance modulus value. It specifically considers the presence of additive noise on both the voltage and current measuring channels. The analytical expression given is important when designing the measurement system to determine the minimum number of samples that should be acquired as well as in computing the confidence intervals for the estimations made using this method.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"6500111-6500111"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11594102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standardized Approach for Assessing Wireless IoT Devices in Millimeter-Wave Industrial Work-Cell Channels","authors":"Mohamed Kashef;Kate A. Remley;Paritosh Manurkar;Joshua M. Kast;Iyemeh E. Uchendu;Robert D. Horansky;Dylan Williams;Matthew Simons;Michael Frey;Lucas Koepke;Richard Candell","doi":"10.1109/OJIM.2026.3679169","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3679169","url":null,"abstract":"We present a standardized, repeatable test approach that replicates key features of measured industrial environments in a fully characterized hybrid test chamber. The hybrid chamber consists of an anechoic chamber along with movable metallic spheres that provide discrete reconfigurable multipath components (MPCs). The chamber is characterized by a synthetic-aperture system providing a reference measurement that captures the nonidealities of the chamber along with the intended spatial multipath, completing characterization of the test environment. A key feature of our approach is the propagation of correlated uncertainties through to the final system response, including uncertainties specific to synthetic apertures such as cable bending due to scanning and inexact positioner locations. The uncertainty analysis provides statistically accurate knowledge of the environment, allowing separation of measurement errors from errors related to the device or system under test. We have also extracted three exemplar channels that capture key features of a work-cell-sized industrial environment from measurements made with the same synthetic aperture. Each exemplar channel presents different, realistic angular-diverse multipath challenges to a millimeter-wave (mmWave) Internet-of-Things device, subsystem, or active antenna under test. This allows engineers to evaluate their hardware designs in a physical, repeatable test environment and also facilitates future standardization of test methods for these and other mmWave wireless devices.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"1-16"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11459359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency-Dependent Uncertainty Analysis of Bioimpedance in Engineered and Native Muscle","authors":"M. Ingrosso;L. D'alvia;M. Cosentino;D. Genovese;C. Bencivenga;L. Apa;Z. del Prete;E. Rizzuto","doi":"10.1109/OJIM.2026.3730555","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3730555","url":null,"abstract":"Bioimpedance-based characterization of engineered muscle systems is inherently affected by frequency-dependent uncertainty, which limits measurement reliability and interpretability in biomedical and bioinspired applications. A systematic understanding of uncertainty propagation across frequency can therefore benefit the robustness of measurements and enable more consistent comparisons between biological tissues, where an unavoidable source of variability arises from the intrinsic biological nature of the specimens. In this work, a frequency-resolved uncertainty framework was applied to impedance measurement of an engineered muscle system (X-MET) and a native muscle reference, both obtained from a murine model. The method integrated instrumental, repeatability, and biological variability contributions to provide a structured decomposition of total uncertainty in the frequency domain, enabling consistent evaluation of reliability metrics such as relative uncertainty. Importantly, uncertainty analysis was extended beyond electrical measurements to include tissue imaging data, since sample dimensional variability was explicitly quantified and incorporated into the normalized impedance evaluation. This allowed the effect of geometric variability to be consistently propagated within the uncertainty model. Results demonstrated that both engineered and native systems exhibit frequency-dependent behavior, with increasing relative uncertainty at higher frequencies and nonuniform contributions across uncertainty sources. Distinct frequency regimes were identified, characterized by different levels of measurement reliability and signal quality. The engineered system showed reproducible and well-defined uncertainty patterns across the spectrum, despite exhibiting higher relative uncertainty than native muscle. Nevertheless, consistent frequency-dependent trends were preserved, supporting the identification of operational frequency regions despite intrinsic biological variability.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"1000411-1000411"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11677138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated System for Multitechnique Contact Angle Measurements Under Pressure and Temperature Control","authors":"Fabian Tapias;Nikolaos Karadimitriou;Holger Steeb;Maartje Boon","doi":"10.1109/OJIM.2026.3731331","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3731331","url":null,"abstract":"Accurate and precise contact angle measurements are critical for characterizing surface wettability and fluid–solid interfacial phenomena in scientific and engineering applications. However, most high-pressure, high-temperature (HPHT) contact angle instruments are limited to static measurements, while dynamic techniques such as tilted-plate analysis with controlled rotation remain restricted to ambient-pressure conditions. This work presents an integrated measurement system enabling both static and dynamic contact angle measurements using sessile-drop, captive-bubble, and tilted-plate techniques with continuous rotation under controlled pressure and temperature. The system integrates a titanium measurement cell designed for operation up to 50 MPa with a heating system capable of reaching <inline-formula> <tex-math>$300~^{circ }$ </tex-math></inline-formula>C. It is coupled with a MATLAB-based image-analysis framework that ensures consistent contour extraction, contact-point detection, and contact angle computation across all techniques, thereby enabling direct comparison between techniques, reducing operator-dependent variability, and improving reproducibility. Algorithmic performance was tested using 1600 synthetic droplet contours spanning contact angles from 5° to 173°. Bland–Altman analysis demonstrated strong agreement between the computed contact angles and the known ground-truth values. Measurement precision and system robustness were assessed using 3-D-printed reference substrates containing embedded droplets with predefined geometries. An uncertainty analysis performed in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM) yielded an expanded uncertainty of <inline-formula> <tex-math>${U} {,}{,}text {=}{,}{,}{4.00}^{circ }$ </tex-math></inline-formula> at approximately 95% confidence. Experimental demonstrations using a water–N2 system on a quartz surface at pressures up to 10 MPa and temperatures up to <inline-formula> <tex-math>$110~^{circ }$ </tex-math></inline-formula>C confirmed stable and repeatable performance across all techniques. The proposed system provides a unified and validated platform for HPHT static and dynamic wettability characterization, achieving measurement precision comparable to commercial instruments and uniquely extending experimental capabilities through continuous-rotation tilted-plate measurements under controlled pressure and temperature conditions.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"7500113-7500113"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11682985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Sample Properties on Short-Circuited Waveguide Measurements for Materials Characterization","authors":"Alexander Hook;Jared Sinkey;Kristen M. Donnell","doi":"10.1109/OJIM.2026.3711321","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3711321","url":null,"abstract":"Microwave materials characterization measurements can be performed using a number of well-established approaches. One such approach, the filled transmission line approach featuring a short-circuited rectangular waveguide (SC-RWG) sample holder, is known to have sample placement restrictions related to measurement viability. This work focuses on this approach and addresses the measurement restrictions within the context of sample length and dielectric properties. The complex reflection properties, S11, of a sample placed in a SC-RWG sample holder are studied to quantitatively define when a sample must be offset from the SC-end. The impact of the sample holder is also studied from a measurement perspective, effectively establishing a (magnitude-based) measurement threshold that quantifies when the total measured signal is affected by the presence of a sample, and hence the complex reflection properties may be used for materials characterization. For cases where the impact of the sample on the measured signal does not exceed the measurement threshold, the sample must be assumed lossless, and the phase of the response may be used to determine permittivity. This is true so long as the impact of the sample on the measured phase is greater than the phase resolution of the measurement equipment. Considering the impact of the sample holder (specific to this work), measurements indicate that samples of length <inline-formula> <tex-math>$lambda _{g}$ </tex-math></inline-formula>/3 or greater (regardless of dielectric properties) do not require the offset.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"6000310-6000310"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11599845","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Numerical Approach and Experimental Validation for MAPOD in Eddy Current Nondestructive Testing","authors":"Mohammad Wasef Abulaila;Mingyang Lu;John C. Aldrin;Jiming Song","doi":"10.1109/OJIM.2026.3736170","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3736170","url":null,"abstract":"Model-assisted probability-of-detection (MAPOD) studies for eddy current (EC) nondestructive testing (NDT) require a large number of flaw evaluations across a wide range of defect dimensions, making computational efficiency a critical consideration. This article presents an efficient numerical framework for characterizing and simulating the response of surface-breaking flaws detected using a reflection differential split-D EC probe. A detailed finite-element model was developed in COMSOL Multiphysics and optimized through the implementation of impedance boundary conditions (IBCs), symmetric meshing strategies, and symmetry plane model reductions to achieve an effective balance between simulation accuracy and computational cost. Single-parameter and multiparameter sweeps were performed to investigate the influence of crack dimensions and liftoff on the differential probe response. The resulting simulation database was used to construct a surrogate model capable of predicting the peak differential impedance response for arbitrary flaw dimensions within the investigated parameter space without requiring direct finite-element evaluation. The surrogate formulation captures the dependence of the response on flaw depth, length, width, and liftoff and achieves agreement with the numerical simulation data with a correlation coefficient of <inline-formula> <tex-math>$R^{2} =97.26$ </tex-math></inline-formula>%. To validate the proposed approach, raster-scan measurements were performed on a set of titanium specimens containing electrical discharge machining (EDM) notches of varying lengths using an automated EC scanning system. The measured responses exhibited trends consistent with the simulation results. Furthermore, the experimental dataset was used to establish a transfer function relating the surrogate model predictions to measured responses, resulting in a correlation coefficient of <inline-formula> <tex-math>$R^{2} = 94.95%$ </tex-math></inline-formula>. The proposed framework provides an efficient pathway for generating large datasets required in MAPOD studies while maintaining strong agreement with experimental observations.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"6000510-6000510"},"PeriodicalIF":2.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11703680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised Autoencoder-Based Anomaly Detection Under Limited Failure Data for Oil Industry","authors":"Victor Z. Ferreira;Eduardo M. Brito;Guilherme M. F. Da Cunha;Felipe A. R. Perez;Erico D. W. Dos Reis;Edfram R. Pereira;Francisco Louzada","doi":"10.1109/OJIM.2026.3670416","DOIUrl":"https://doi.org/10.1109/OJIM.2026.3670416","url":null,"abstract":"This article presents a deep autoencoder-based methodology for unsupervised anomaly detection in centrifugal pumps under limited failure data conditions, focusing on real-world applications in the oil and gas industry. The autoencoders are designed to reconstruct input sequences from supervisory control and data acquisition (SCADA) data, including pressure, temperature, vibration, and electric current, with anomaly detection derived from the reconstruction error. Anomaly detection is based on a moving-minimum anomaly score (MMAS), designed to suppress transient fluctuations and highlight consistent deviations. Each autoencoder was trained on prefault operational windows and evaluated on sequences encompassing registered failures. The autoencoder detected anomalies up to 74 days before the actual fault detection by the maintenance team, with consistent warnings of two to three days in additional cases. The architecture, training parameters, and data preprocessing steps are proposed, including pressure differential computation and idle-state filtering. The findings demonstrate the viability of unsupervised deep learning in industrial fault detection scenarios characterized by limited failure annotations in an industrial environment.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"5 ","pages":"1-12"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11421588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}