Lubricating oil is essential for the proper operation of mechanical equipment. During service, metal debris produced by wear, corrosion or component damage inevitably accumulates in the oil and serves as an important indicator of equipment health. Accurate measurement of metal debris content is therefore of great significance for condition monitoring and predictive maintenance. This article presents a microstrip resonant sensor with a dual “H”-shaped symmetric slotted structure for quantitative detection of metal debris in lubricating oil. An effective relative permittivity model of the oil-debris mixture is established based on Maxwell-Garnett theory, providing the theoretical foundation for sensor analysis. The variation laws of the resonant frequency fr, quality factor Q and reflection coefficient S11 with iron powder volume fraction are investigated by simulation and experiment. By integrating these three parameters into a multiple linear regression model, the maximum relative error of the predicted iron powder volume fraction is reduced to within ±10%, demonstrating clear advantages over conventional single-parameter approaches.
Continuous water-quality measurement is a critical instrumentation challenge for modern, data driven aquaculture. Environmental parameters such as dissolved oxygen (DO), total ammonianitrogen, nitrate-nitrogen, pH, and ambient temperature directly govern fish metabolic rates, feed conversion efficiency, and mortality risk. Despite the proven benefits of real-time monitoring, many small to medium-scale pond operators still rely on labor-intensive manual sampling or low-cost analog Internet-of-Things (IoT) prototypes. These traditional analog systems are highly vulnerable to sensor drift, electrical noise, and poor long-term submerged reliability. This Short Scientific Communication presents Tepai, a robust, floating, solar powered IoT monitoring station that integrates industrial-grade RS-485/Modbus digital water quality probes with a low-cost ESP32-based acquisition platform and a custom web dashboard. By leveraging digital sensor interfaces, the system provides continuous, noise-immune measurement of complex parameter including ammonia and nitrate while maintaining a portable form factor suitable for long-term, autonomous pond deployment.
Reliable optical inspection of root food products is limited by local tissue heterogeneity, irregular shape, and unstable single-point spectra. This Short Scientific Communication presents a local consistency measurement framework for visible and near infrared (Vis/NIR) authentication under limited band conditions. The method treats each region of interest (ROI) as a local measurement unit, learns ROI level spectral evidence, and converts repeated local decisions into an inspection level output. A local consistency index (LCI) is further used to quantify whether the repeated ROI measurements support the final decision or require review. Using a four-category root food dataset with 600 ROI spectra and 31 spectral bands, the framework improved the practical inspection accuracy from 79.67% at the single ROI level to about 92.7% after local consistency based decision aggregation.
Auscultation remains a cornerstone of pre-hospital clinical assessment, providing rapid, non invasive insight into cardiopulmonary function during time-critical emergency care. In ambulance environments, however, the diagnostic reliability of auscultation is significantly compromised by high-intensity ambient noise sources, including engine vibration, road noise, and siren acoustics, which often exceed 90�100 dB. These conditions degrade signal fidelity, obscure clinically relevant acoustic features, and increase the likelihood of diagnostic error or delayed intervention. This paper presents a comprehensive review and technical synthesis of the challenges associated with ambulance-based auscultation, its clinical significance in emergency medical services, and the emerging technological solutions designed to mitigate noise interference. Auscultation's clinical importance in pre-hospital settings is first examined, emphasizing its role in rapid diagnosis, risk stratification, and therapeutic decision-making. Lung and heart sounds provide essential diagnostic cues for conditions such as pneumothorax, acute heart failure, airway obstruction, and chronic obstructive pulmonary disease [1]. When integrated with patient history, vital signs, and adjunct tools, auscultatory findings contribute significantly to diagnostic accuracy and early intervention. However, the interpretive nature of auscultation and its dependence on acoustic clarity make it particularly vulnerable to environmental disturbances. The multifactorial challenges inherent in ambulance environments, including acoustic masking, motion artifacts, and reduced measurement reliability, are then analyzed. Experimental and clinical evidence demonstrate substantial reductions in auscultation accuracy under transportation conditions, with detection rates and diagnostic sensitivity significantly impaired compared to controlled clinical environments. These limitations highlight the need for robust technological interventions.
To address the safety hazards posed by the structural degradation of rock formations in the Dazu Thousand-Handed Guanyin monument and to evaluate its geological stability, comprehensive geotechnical investigations were undertaken using borehole television and digital borehole camera technologies. This study provides a scientific foundation for cultural relic preservation and introduces an innovative methodology for engineering maintenance. Systematic application of imaging techniques enabled the collection of quantitative borehole image data through detailed geotechnical evaluations. Subsequent statistical analyses characterized the distribution patterns of rock strata structures within the Bodhisattva formation, examined fracture development characteristics, and assessed overall rock mass stability. The findings identified three dominant joint sets with strike directions of NE10°–30°, NE80°–90°, and NW280°–310°, respectively. These tectonically induced fractures exhibit significant aperture dimensions, with maximum widths reaching up to 1 meter. While the overall rock mass remains relatively intact, localized destabilization is observed at fracture intersection zones, posing potential safety risks. These results offer empirical support and decision-making frameworks for conservation strategies and serve as technical references for geotechnical safety assessments of cultural heritage sites.
Global demographic dynamics are increasingly marked by a sustained shift toward older populations. This demographic transition is reshaping healthcare system priorities, which must move from managing acute illness to supporting long-term functional independence. In response, the World Health Organisation (WHO) launched the Decade of Healthy Ageing (2021–2030), a global initiative based on a critical insight: quality of life in later years depends not only on the absence of disease, but also on the preservation of an individual's intrinsic capacity (IC) [1]. Defined as the composite of all physical and mental capacities an individual can draw upon at any point in time, IC includes five interrelated domains: cognition, vitality, locomotion, psychological capacity, and sensory function [2]. Importantly, IC is not fixed. It responds to lifestyle, social environment, and health interventions, so timely and accurate monitoring offers a real opportunity to preserve functional independence and delay decline. However, IC is also individual: ageing trajectories differ significantly across people, shaped by the cumulative interaction of behavioural patterns, social connectedness, genetic predisposition, and environmental context [3].
In instrumentation and measurement, the estimation and propagation of measurement uncertainty is a persistent “open problem” where traditional analytical methods become impractical or unreliable. Many real-world measurement models involve non-Gaussian distributions, correlated input data, and complex functional relationships, which makes classical approaches difficult to apply. The Monte Carlo method (MCM) does not require linearization of the measurement model and correctly works with nonlinear dependencies and also allows the use of many types' distributions for input variables. An algorithm is proposed for analyzing experimental data, approximating them using different types of regression models and obtaining the predicted value of the studied variable with a confidence interval estimated using MCM. For the predicted value of the inductance standard, modeling was performed using the MCM method for 3rd order polynomial regression for two different long-term periods. For the same values of the long-term time drift of the standard, regression analysis was also applied. Compatible results were obtained within the established confidence interval. Calibration laboratories should take this significant factor into account when establishing or verifying the intercalibration intervals of their own standards.
3D fluorescence spectroscopy is commonly used for water pollutant identification and analysis, but traditional systems for acquiring and processing 3D fluorescence spectra are not suitable for portable instruments with limited resources due to the bulky optical scanning device, large data volume and computational complexity. In this paper, a portable water pollutant detection system based on 3D fluorescence spectra with a sparse reconstruction-encoding strategy is proposed. In the system, a multi-wavelength LED-filter synergy module was designed to capture sparse 3D fluorescence spectra. Additionally, a super-resolution reconstruction algorithm based on dictionary learning is integrated, enhancing the dimensionality and information of sparse data. To make the algorithm lightweight for direct implementation on the instrument, centroid-guided feature points are extracted in layers, with an efficient and lightweight joint encoding method introduced to consider both positional and intensity correlations between feature points. Then, a Histogram Intersection Kernel Support Vector Machine is implemented on an embedded platform for identification of water pollutants. Validation experiments on six common water pollutants show that the proposed method outperforms conventional approaches, offering an innovative and practical solution for portable on-site water pollutant detection.
Electrical activity is a fundamental manifestation of physiological function. Signals generated by organs such as the heart, brain, muscles, and eyes can be measured and used for diagnosis, monitoring, and control. Visual electrophysiological signals are particularly relevant because they provide noninvasive access to eye movements, retinal activity, cortical processing, and the visual pathway [1].
The palm oil industry, a central component of Southeast Asia’s economy, is facing growing pressure to enhance productivity, sustainability, and labor efficiency amid global environmental and market challenges. Recent advances in instrumentation, unmanned aerial vehicles (UAVs), and deep learning have enabled new opportunities for intelligent measurement and monitoring of palm fruit growth, health, and yield. This Short Review/Roadmap paper presents a regional perspective on the evolution, current state, and future direction of drone- and AI-based instrumentation for palm fruit detection and yield estimation in Malaysia, Indonesia, and Thailand. The review traces the transition from manual inspection and satellite observation to high-resolution UAV imaging integrated with convolutional neural networks (CNNs), YOLO-based object detection, and hyperspectral data analytics. It also highlights hardware and software developments in multispectral imaging, sensor calibration, and edge-AI deployment for real-time plantation monitoring. Despite rapid progress, major challenges remain, including limited labeled datasets, variable lighting and canopy density, and high operational costs for large-scale automation. This paper identifies these gaps and outlines a roadmap toward affordable, data-driven, and sustainable measurement systems through regional collaboration, open datasets, and hybrid sensing architectures. The discussion aims to guide researchers, policymakers, and industry practitioners toward the next generation of precision instrumentation for smart palm agriculture across Southeast Asia.
The characteristics of cesium fountain clocks are different from those of timekeeping atomic clocks. Cesium fountain clocks possess extremely high accuracy and excellent long-term stability. Since the operation of cesium fountain clocks is intermittent, and timekeeping systems often require continuous information on the frequency difference of a single clock relative to cesium fountain clocks as a reference for the frequency calibration of atomic clocks, relevant research on the prediction of frequency differences is carried out in this paper. The operational characteristics and performance indicators of cesium fountain clocks are introduced. A dual-scale Hampel filter is proposed that consists of a large scale and a small scale. The small scale captures the instantaneous anomalies and missing values of the frequency difference of hydrogen masers relative to cesium fountain clocks, while the large scale smooths the slow-varying trend of the frequency difference of hydrogen masers relative to cesium fountain clocks. Based on this filter, the experimental results show that the intermittent missing values are effectively estimated, making the data complete and continuous. When the time scale is calculated with this continuous data as a reference, the trend term of the time scale is significantly reduced.
Learning by doing is a cornerstone of technical education and plays a vital role across all engineering disciplines, particularly in instrumentation and measurement applications [1]. Furthermore, when the topics addressed in coursework and laboratory activities have a direct application impacting the community, students become highly motivated to apply the theoretical concepts and, consequently, to further explore and deepen their technical skills learned in the classroom [2], [3]. This concept is in full correspondence with the IEEE's mission to advance technology for humanity. The student project presented in this paper uses a sensor-based system which monitors air quality through its operation in a hybrid wireless system. The system provides users with uninterrupted data access through its combination of cloud resources and smartphone connectivity features. To support preventive decision-making, an Autoregressive Integrated Moving Average (ARIMA) model is implemented to forecast short-term air quality trends based on historical data [4]. The system uses a naive Bayes classifier to determine risk levels for people who have been exposed to different situations. A cloud-based database stores cumulative daily exposure metrics and facilitates long-term trend analysis. The prediction and classification system allows for modular design which enables users to test different methods including AI-based models for system improvement purposes.
This communication aims to present an introduction to Quartz Crystal Microbalance (QCM) sensors and how they can be integrated into IoT applications in a complete energy-harvesting or passive wireless form. A Quartz Crystal Microbalance (QCM) is a resonator-based sensor where the QCM is set into oscillation at resonant frequency, and any changes in the parameter being measured would lead to a change in the resonant frequency of the QCM. An AT-cut quartz crystal disc, sandwiched between two metal electrodes, forms the core of a QCM device. Quartz substrate is known for its piezoelectric properties, which allow it to convert applied electrical signals into mechanical vibrations and, conversely, to generate electrical signals in response to mechanical deformation. As shown in Fig. 1 a), utilizing vibrations in the transversal mode through the thickness of the quartz crystal, an electrical signal can be converted into a vibration at one electrode and reconverted back into an electrical signal at the opposite electrode. When particles or masses land on the AT-cut Quartz, a change in mass of the overall structure of the QCM occurs, which in turn leads to a change in resonant frequency of the QCM. The QCM is capable of measuring the changes in mechanical strain brought about by mass-loading onto the crystal, following Sauerbrey's principle [1], where the change in mass (Δm in kg) alters the resonance frequency (f0 in Hz) of the QCM as given by the equation below.
Global Navigation Satellite System (GNSS) now provides global users with high-precision positioning, navigation, and timing services. Precise point positioning (PPP) technique has been developed and utilized for international atomic time link computations. However, PPP relies on high-precision satellite products to correct satellite-related errors in measurement observations output by a GNSS receiver. To support applications, precise satellite products were provided either via network transmission or through direct satellite broadcast in real-time. In recent years, network-based products, which are encrypted, internationally standardized, and transmitted in binary format, and satellite-based products, which are fragmented, customized, encoded, and transmitted in binary format, have made it more difficult for users to reliably and continuously recover them. At present, real-time open-source PPP software does not provide these two supports, which limits the application of real-time PPP, particularly for stable and continuous real-time PPP time transfer research and implementation. In this study, two supporting software packages have been developed and released as open source, designed to be embedded into mainstream open-source C/C++ PPP software. RTStreamHub is a support package for receiving and processing encrypted network-based products, designed to ensure reliable, real-time reception of securely transmitted satellite binary products. HASPPP is a dedicated software for Galileo satellite-based PPP, providing integrated support for recovering complex satellite-based products and implementing PPP, while also offering interfaces for seamless integration into mainstream C/C++ software for PPP time transfer applications. The real-time PPP time transfer precision of both network- and satellite-based products was analyzed using the PPP model embedded within the software HASPPP. Taking fiber-optic link results as a reference, the PPP time transfer precision is 125.9 ps based on network-based satellite products and 133.8 ps based on Galileo satellite-based products. The performance of these software packages has been validated, providing support modules for mainstream C/C++ PPP software.
In precise point positioning (PPP) high-precision time comparison, the estimated receiver clock bias exhibits discontinuity at day boundary. Usually, the time comparison between two time keeping laboratories requires long-term and continuous in practical applications. Therefore, the discontinuous receiver clock bias cannot truly reflect the time difference between two time keeping laboratories, and the performance evaluation and synchronization control of the time comparison were impacted. In this paper, the correlation of state vector and covariance matrix between epochs in PPP data processing was used, the state vector and covariance matrix from the final epoch of the preceding day were used as the prior values for the first epoch on the subsequent day, the convergence time of the initial estimate for the next day was reduced, and the accuracy of the time comparison of clock bias was improved. Relying on the PPP time comparison links among major international time keeping laboratories, the prior information of state vector and covariance matrix were used in PPP time comparison estimation algorithm, and the clock bias discontinuity at the day boundary has been improved effectively. The results show that the PPP time comparison technology is used between different continents, and the state vector and covariance matrix from the final epoch of the preceding day were used as the prior values for the first epoch on the subsequent day, the clock bias of multi-day continuous estimation discontinuity at the day boundary is improved compared with the single-day estimation, the variation of clock bias between epochs at day boundary has decreased significantly, the Allan deviation (ADEV) and Time deviation (TDEV) values of clock bias have decreased, and the performance of ADEV and TDEV have increased by more than 30%.
The prediction of remaining fatigue life (RFL) for drive axles is crucial in mechanical engineering, particularly in electric vehicles where data scarcity from real-world fatigue monitoring poses significant challenges. This study presents a novel two-stage approach for residual fatigue life prediction. In the first stage, a bidirectional generative adversarial network (BiGAN) is employed to generate degraded data and extract the corresponding health index. In the second stage, a multiscale dual-fusion attention-based bidirectional long short term memory (MDFA-BiLSTM) network is used to map the observed data to the health index and perform regression-based prediction of the remaining fatigue life. This approach enables enhanced predictive maintenance in the automotive industry, reducing downtime, mitigating safety risks, and lowering operational costs in engineering applications.
Dr. Judy Amanor-Boadu one of our current Instrumentation and Measurement Society (IMS) Administrative Committee (AdCom) members, is a true “Rising Star” in both IEEE and IMS, and as evidence, she was formally selected by the 2025 IEEE TAB Vice President (VP), Dr. Dalma Novak, to be a keynote speaker and represent the TAB VP at the IEEE 2025 Rising Stars event. This event is for students and young professionals and is meant to help them develop their careers through networking, technical talks, and workshops.
This study addresses the need for precise force control in robotic end-effectors by developing an intelligent gripper based on Fiber Bragg Grating (FBG) sensing. Finite element analysis optimized the FBG embedding parameters, determining the ideal depth. A four-channel FBG force-wavelength coupling model was established, with temperature compensation achieved through elimination calculation. Calibration results show that temperature compensation significantly improved the force-wavelength relationship: 2 increased from 0.62-0.92 to 0.98-0.99, while RMSE decreased by 61.8% (from 2.75 & times; 10(-6) to 1.05 & times; 10(-6)). An overdetermined equation system was constructed to derive the force-wavelength matrix $K$, achieving 99% fitting accuracy. The closed-loop control system maintained stable performance in constant-force gripping tests, with force errors within +/- 0.2 N, demonstrating its potential for precision applications like assembly and surgery.
In this article, I continue my interviews with Instrumentation and Measurement Society (IMS) award winners announced at the 2025 I2MTC in Chemnitz, Germany. These individuals are examples of excellence who the IMS Award Committee has chosen because of their technical expertise and their service to the profession. In particular, the J. Barry Oakes Award is given to an individual 35 years of age or younger who best meets the criteria of technical contributions to I&M science and engineering and leadership/project management skills or service.
Nowadays, technology advancements in high-quality laptops and smartphones and the evolution of artificial intelligence have contributed for face recognition to be widely utilized in several emerging applications, such as security, law-enforcement, access control, attendance control, forensic recognition, fraud prevention, and commerce. The main benefit of face recognition over other authentication modalities like passwords and keycards relies on the unique aspect of the human face, which allows precise measurements of the individual biometric features.
