Search Results (208)

In this study, an accurate attenuation measurement system with high attenuation capability (≥100 dB) is presented, covering a broad radio frequency range from 1 GHz to 25 GHz. The system employs a dual-channel intermediate frequency (IF) substitution method, utilizing a programmable inductive voltage divider (IVD) that provides precise voltage ratios at a 1 kHz operating IF, serving as the primary attenuation standard. To ensure optimal inter-channel isolation, essential for accurate high-attenuation measurements, an optical fiber assembly, consisting of a laser diode, a wideband external electro-optic modulator, and a photodetector, is integrated between the channels. A comprehensive performance evaluation is presented, with particular emphasis on the programmable IVD calibration technique, which achieves an accuracy better than 0.001 dB across all attenuation levels, and on the role of the optical fiber assembly in enhancing isolation, demonstrating levels exceeding 120 dB across the entire frequency range. The system demonstrates measurement capabilities with expanded uncertainties (k = 2) of 0.004 dB, 0.008 dB, and 0.010 dB at attenuation levels of 20 dB, 60 dB, and 100 dB, respectively. Full article

This study presents a self-powered smart wrist brace integrated with a piezoelectric sensor for real-time biomechanical monitoring during weightlifting activities. The system was designed to quantify wrist flexion across multiple loading conditions (0 kg, 0.5 kg, and 1.0 kg), leveraging mechanical strain-induced voltage generation to capture angular displacement. A flexible PVDF film was embedded within a custom-fitted wrist brace and tested on male and female participants performing controlled wrist flexion. The resulting voltage signals were analyzed to extract root-mean-square (RMS) outputs, calibration curves, and sensitivity metrics. To interpret the experimental results analytically, a lumped-parameter cantilever beam model was developed, linking wrist flexion angles to piezoelectric voltage output based on mechanical deformation theory. The model assumed a linear relationship between wrist angle and induced strain, enabling theoretical voltage prediction through simplified material and geometric parameters. Model-predicted voltage responses were compared with experimental measurements, demonstrating a good agreement and validating the mechanical-electrical coupling approach. Experimental results revealed consistent voltage increases with both wrist angle and applied load, and regression analysis demonstrated strong linear or mildly nonlinear fits with high $R^2$ values (up to 0.994) across all conditions. Furthermore, surface plots and strain sensitivity analyses highlighted the system’s responsiveness to simultaneous angular and loading changes. These findings validate the smart wrist brace as a reliable, low-power biomechanical monitoring tool, with promising applications in injury prevention, rehabilitation, and real-time athletic performance feedback. Full article

This paper presents the design, fabrication, calibration, and comprehensive characterization of a homemade tri-axial fluxgate magnetometer. The magnetometer, utilizing a ring core configuration, was developed to measure ultra-low magnetic fields with high sensitivity and stability. Critical stages from material selection to sensor geometry optimization are discussed in detail. A series of critical characterization processes were conducted, including zero-field voltage determination, scale factor calculation, resolution measurement, noise analysis, bias assessment, cross-field effect evaluation, temperature dependency, and bandwidth determination. The sensor demonstrated a minimum detectable magnetic field resolution of 2.2 nT with a noise level of 1.1 nT/√Hz at 1 Hz. Temperature dependency tests revealed minimal impact on sensor output with a maximum shift of 120 nT in the range of 60 °C, which was effectively compensated through calibration to less than 5 nT. Additionally, the paper introduces a model function in matrix form to relate the magnetometer’s output voltage to the measured magnetic field, incorporating temperature dependency and cross-field effects. This work highlights the importance of meticulous calibration and optimization in developing fluxgate magnetometers suitable for various applications, from space exploration to biomedical diagnostics. Full article

This paper presents the design, development, and characterization of a low-cost and environmentally friendly high-voltage divider optimized for on-site calibration up to 850 kV. Unlike traditional dividers that rely on oil or SF₆ for insulation, both of which pose environmental risk and regulation issues, the proposed system uses modular construction with commercial off-the-shelf components and natural air insulation, minimizing environmental impact and facilitating transport, calibration, and maintenance. Despite using air insulation, the divider demonstrates excellent uncertainty performance. Characterization results show frequency linearity better than 0.2% up to 100 kHz and a bandwidth exceeding 10 MHz, making it suitable for the measurement of a wide range of voltage types. Static and dynamic performance evaluations confirm reliable scale factor stability and low measurement uncertainty: 0.01% for DC (550 kV), 0.3% for AC (405 kV), and 0.7% for impulses such as 1.2/50 µs (850 kV). The system offers a practical and sustainable solution for high-voltage measurements, meeting growing industrial and European environmental demands. Full article

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential for enhancing longevity. Automotive PEMFC systems are complex and nonlinear, making lifespan prediction difficult. Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. Parametric analysis shows optimal accuracy with the grouping parameter set to 4. Experimental validation, with a voltage lifetime threshold at 96% (3.228 V), shows the predicted degradation closely aligns with actual results. The model predicts voltage threshold times at 809 h and 876 h, compared to actual values of 807 h and 872 h, with a temporal prediction error margin of 0.250–0.460%. These results demonstrate the model’s high prediction fidelity and support proactive health management of PEMFC systems. Full article

Dynamic comparators play an important role in electronic systems, requiring high accuracy, low power consumption, and minimal offset voltage. This work proposes an accurate and low-complexity offset calibration design based on a capacitive load approach. It was designed using a 65 nm CMOS technology and comprehensively evaluated under Monte Carlo simulations and PVT variations. The proposed scheme was built using MIM capacitors and transistor-based capacitors, and it includes Verilog-based calibration algorithms. The proposed offset calibration is benchmarked, in terms of precision, calibration time, energy consumption, delay, and area, against prior calibration techniques: current injection via gate biasing by a charge pump circuit and current injection via parallel transistors. The evaluation of the offset calibration schemes relies on Analog/Mixed-Signal (AMS) simulations, ensuring accurate evaluation of digital and analog domains. The charge pump method achieved the best Energy-Delay Product (EDP) at the cost of lower long-term accuracy, mainly because of its capacitor leakage. The proposed scheme demonstrated superior performance in offset reduction, achieving a one-sigma offset of 0.223 mV while maintaining precise calibration. Among the calibration algorithms, the window algorithm performs better than the accelerated calibration. This is mainly because the window algorithm considers noise-induced output oscillations, ensuring consistent calibration across all designs. This work provides insights into the trade-offs between energy, precision, and area in dynamic comparator designs, offering strategies to enhance offset calibration. Full article

This paper presents an ultrasonic driving and detection system based on a CMUT array using MEMS technology. Among them, the core component CMUT array is composed of 8 × 8 CMUT array elements, and each CMUT array element contains 6 × 6 CMUT units. The collapse voltage of a single CMUT unit obtained through finite element analysis is 95.91 V, and the resonant frequency is 3.16 MHz. The driving section achieves 64-channel synchronous driving, with key parameters including an adjustable excitation signal frequency ranging from 10 kHz to 5.71 MHz, a delay precision of up to 1 ns, and an excitation duration of eight pulse cycles. For the echo reception, a two-stage amplification circuit for high-frequency weak echoes with 32 channels was designed, achieving a gain of 113.72 dB and −3 dB bandwidth of 3.89 MHz. Simultaneously, a 32-channel analog-to-digital conversion based on a self-calibration algorithm was implemented, with a sampling rate of 50 Mbps and a data width of 10 bits. Finally, the experimental results confirm the successful implementation of the driving system’s designed functions, yielding a center frequency of 1.4995 MHz and a relative bandwidth of 127.9%@−6 dB for the CMUT operating in silicone oil. This paper successfully conducted the transmit–receive integrated experiment of the CMUT and applied Butterworth filtering to the echo data, resulting in high-quality ultrasonic echo signals that validate the applicability of the designed CMUT-based system for ultrasonic imaging. Full article

The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the calibration of hybrid operating strategies. This paper presents a dynamic closed-loop validation of a hardware-in-the-loop testbench designed for the virtual calibration of hybrid operating strategies for a plug-in hybrid electric vehicle. Requirements regarding the hardware-in-the-loop testbench accuracy are defined based on the investigated use case. From this, a dedicated hardware-in-the-loop testbench setup is derived, including an electrical setup as well as a plant simulation model. The model is then operated in a closed loop with a series production hybrid control unit. The closed-loop validation results demonstrate that the chassis simulation reproduces driving resistance closely aligning with the reference data. The driver model follows target speed profiles within acceptable limits, achieving an R² = 0.9993, comparable to the R² reached by trained human drivers. The transmission model replicates the gear ratios, maintaining rotational speed deviations below 30 min⁻¹. Furthermore, the shift strategy is implemented in a virtual control unit, resulting in a gear selection comparable to reference measurements. The energy flow simulation in the complete powertrain achieves high accuracy. Deviations in the high-voltage battery state of charge remain below 50 Wh in a WLTC charge-sustaining drive cycle and are thus within the acceptable error margin. The net energy change criterion is satisfied with the hardware-in-the-loop testbench, achieving a net energy change of 0.202%, closely matching the reference measurement of 0.159%. Maximum deviations in cumulative high-voltage battery energy are proven to be below 10% in both the charging and discharging directions. Fuel consumption and CO₂ emissions are modeled with deviations below 3%, validating the simulation’s representation of vehicle efficiency. Real-time capability is achieved under all investigated operating conditions and test scenarios. The testbench achieves a real-time factor of at least 1.104, ensuring execution within the hard real-time criterion. In conclusion, the closed-loop validation confirms that the developed hardware-in-the-loop testbench satisfies all predefined requirements, accurately simulating the behavior of the reference vehicle. Full article

With the increasing complexity of power systems, the monitoring data of UPFC submodules suffers from high missing rates due to sensor failures and environmental interference, significantly limiting equipment condition assessment and fault warning capabilities. To overcome the computational complexity, poor real-time performance, and limited generalization of existing methods like GRU-GAN and SOM-LSTM, this study proposes a hybrid framework combining Bayesian multiple imputation with a Support Vector Machine (SVM) for data repair. The framework first employs an adaptive Kalman filter to denoise raw data and remove outliers, followed by Bayesian multiple imputation that constructs posterior distributions using normal linear correlations between historical and operational data, generating optimized imputed values through arithmetic averaging. A kernel-based SVM with RBF and soft margin optimization is then applied for nonlinear calibration to enhance robustness and consistency in high-dimensional scenarios. Experimental validation focusing on capacitor voltage, current, and temperature parameters of UPFC submodules under a 50% missing data scenario demonstrates that the proposed method achieves an 18.7% average error reduction and approximately 30% computational efficiency improvement compared to single imputation and traditional multiple imputation approaches, significantly outperforming neural network models. This study confirms the effectiveness of integrating Bayesian statistics with machine learning for power data restoration, providing a high-precision and low-complexity solution for equipment condition monitoring in complex operational environments. Future research will explore dynamic weight optimization and extend the framework to multi-source heterogeneous data applications. Full article

High-voltage direct-current (HVdc) transmission lines are gaining more attention as an integral part of modern power system networks. Monitoring the dc current is important for metering and the development of dynamic line rating control schemes. However, this has been a challenging task, and there is a need for wireless sensing methods with high accuracy and a dynamic range. Conventional methods require direct contact with the high-voltage conductors and utilize bulky and complex equipment. In this paper, an ultra-high-frequency (UHF) radio frequency identification (RFID)-based sensor is introduced for the monitoring of the dc current of an HVdc transmission line. The sensor is composed of a passive RFID tag with a custom-designed antenna, integrated with a Hall effect magnetic field device and an RF power harvesting unit. The dc current is measured by monitoring the dc magnetic field around the conductor using the Hall effect device. The internal memory of the RFID tag is encoded with the magnetic field data. The entire RFID sensor can be wirelessly powered and interrogated using a conventional RFID reader. The advantage of this approach is that the sensor does not require batteries and does not need additional maintenance during its lifetime. This is an important feature in a high-voltage environment where any maintenance requires either an outage or special equipment. In this paper, the detailed design of the RFID sensor is presented, including the antenna design and measurements for both the RFID tag and the RF harvesting section, the microcontroller interfacing design and testing, the magnetic field sensor calibration, and the RF power harvesting section. The UHF RFID-based magnetic field sensor was fabricated and tested using a laboratory experimental setup. In the experiment, a 40 mm-diameter-aluminum conductor, typically used in 500 kV HVdc transmission lines carrying a dc current of up to 1200 A, was used to conduct dc current tests for the fabricated sensor. The sensor was placed near the conductor such that the Hall effect device was close to the surface of the conductor, and readings were acquired by the RFID reader. The sensitivity of the entire RFID sensor was 30 mV/mT, with linear behavior over a magnetic flux density range from 0 mT to $4.5$ mT. Full article

Thermal management is pivotal for ensuring the safe and efficient operation of LIBs under dynamic conditions. Accurate core temperature monitoring remains a key BTMS challenge for predicting thermal distributions and mitigating TR risks. This study proposes a real-time core temperature estimation framework integrating joint EKF with an electro-thermal-aging model (ECM-1D). Using only surface temperature and voltage measurements, it simultaneously estimates core temperature, SOC, and capacity with bidirectional electro-thermal coupling. The hybrid approach pre-calibrates temperature/SOC/SOH-dependent parameters offline while updating capacity online. Validation under extreme conditions (high-rate cycling, aging, and ISCs) demonstrates 60% lower core temperature RMSE during high-rate cycling, a maximum estimation error below 0.9 K, and 58.9% reduction in SOC estimation error under aging conditions versus existing methods. The framework reliably tracks core temperature trends despite parasitic heat and signal noise, enabling earlier critical temperature warnings. This provides a foundation for TR prevention, advancing battery safety for EV and grid storage applications. Future extensions could integrate physical aging mechanisms and enhance fault detection capabilities. Full article

An innovative design of a hydrophone based on a piezoelectric composite film of $AlN/S{c}_{0.2}A{l}_{0.8}N$ is presented. By designing a non-uniform composite sensitive layer, the dielectric loss and defect density are significantly reduced, while the high-voltage electrical characteristics of scandium-doped aluminum nitride are retained. X-ray diffraction analysis shows that the sensitive films have excellent crystal quality (FWHM is 0.34°). According to the standard underwater acoustic calibration test, the device exhibits full directivity with a minimum deviation of ±0.5 dB at 1 kHz frequency, sound pressure sensitivity of −162.9 dB (re: 1 V/$\mathsf{\mu }$Pa) and equivalent noise density of 46.1 dB (re: 1 $\mathsf{\mu }$Pa/√Hz). The experimental results show that the comprehensive performance of the piezoelectric heterostructure hydrophone meets the standard of commercial high-end hydrophones while maintaining mechanical stability, and provides a new solution for underwater acoustic sensing. Full article

This paper presents a method to recover the negative threshold voltage shift during high field gate oxide screening of 1.2 kV 4H-SiC MOSFETs with an additional adjustment gate voltage pulse. To reduce field failure rates of the MOSFETs in operation, manufacturers perform a screening treatment to remove devices with extrinsic defects in the oxide. Current gate oxide screening procedures are limited to oxide fields at or below ~9 MV/cm for short durations (<1 s), which is not enough to remove all the devices with extrinsic defects. The results show that by implementing a lower field gate pulse, the threshold voltage shift can be partially recovered, and therefore the maximum screening field and time can be increased. However, both the initial screening pulse and the adjustment pulse require careful calibration to prevent significant degradation of the device threshold voltage, on-resistance, interface state density, or intrinsic lifetime. With a well calibrated set of pulses, higher screening fields can be utilized without significantly damaging the devices. This leads to an improvement in the overall screening efficiency of the process, reducing the number of devices with extrinsic oxide defects entering the field, and improving the reliability of the SiC MOSFETs in operation. Full article

In this study, a tubular spot-welded grating sensor composed of a stainless-steel tube fixed to a substrate surface by welding is developed, and the tube is filled with high-performance epoxy resin components after the grating sensor is passed through it. A smart steel strand cable is created by spot welding steel strands using portable spot-welding equipment. This method generates a small current during spot welding, with a voltage of only 3 V to 5 V, and does not damage the internal structure of the steel strand. An equation related to the temperature, tension force, and wavelength fluctuation is presented in this article. A method with a transverse temperature coordinate and a longitudinal wavelength coordinate is used. A formula for the standard temperature calibration of the force values and a procedure for temperature adjustment of the force values are presented. The correlation coefficient between the stress on the steel strand and the wavelength of the tubular spot-welded grating sensor is as high as 0.999 according to static tensile testing, demonstrating good repeatability. The temperature adjustment coefficient for varying temperatures is 0.0264 nm/°C, and the test error is essentially limited to 3.0% F.S. When subjected to a 120 h relaxation test, the steel strand with the tubular spot-welded grating sensor exhibits a relaxation rate of 4.44%. The force value obtained after the relaxation test is 1.2% off from the standard load. A tubular spot-welded grating sensor is welded onto a steel strand within a cable sealing cylinder to create an extruded anchor epoxy-coated steel strand cable. The measured cable force is compared with the standard load. The maximum error is 0.5% F.S. The discrepancy between the measured cable force and the acceleration sensor value is 1.5% in one instance involving an arch bridge employing six smart suspension cables to detect cable forces onsite. The findings provide theoretical and engineering references for smart cables and demonstrate the high accuracy, dependability, and fixation performance of the tubular spot-welded grating sensor smart cable. Full article

Recently, Air Quality Monitoring (AQM) has gained significant R&D attention from academia and industries, leading to advanced sensor-enabled IoT solutions. Literature highlights the use of nanomaterials in sensor design, emphasising miniaturisation, enhanced calibration, and low voltage, room-temperature operation. Significant efforts are aimed at improving sensitivity, selectivity, and stability, while addressing challenges like high power consumption and drift. The integration of sensors with IoT technology is driving the development of accurate, scalable, and real-time AQM systems. This paper provides technical insights into recent AQM advancements, focusing on air pollutants, sensor technologies, IoT frameworks, performance evaluation, and future research directions. It presents a detailed analysis of air quality composition and potential air pollutants. Relevant sensors are examined in terms of design, materials and methodologies for pollutant monitoring. A critical review of IoT frameworks for AQM is conducted, highlighting their strengths and weaknesses. As a technical contribution, an experimental performance evaluation of three commercially available AQM systems in the UK is discussed, with a comparative and critical analysis of the results. Lastly, future research directions are also explored with a focus on AQM sensor design and IoT framework development. Full article