Search Results (260)

This work presents the design and validation of a low-cost electronic architecture for nuclear pulse conditioning and radiation exposure measurement using a Geiger–Müller tube. The main contribution is a structured three-stage conditioning system capable of transforming high-voltage analog nuclear pulses into standardized TTL-compatible digital signals for real-time acquisition and pulse counting. The proposed architecture integrates a regulated 500 V high-voltage supply, voltage coupling and limitation, CMOS-based inversion, and monostable pulse shaping using a 555 timer to generate stable 5 V output pulses with approximately 1600 μs duration. Experimental evaluation included oscilloscope-based pulse characterization, plateau-region verification, and calibration tests performed with a certified gamma radiation source under controlled laboratory conditions. The measured exposure response followed the expected inverse-distance radiation behavior, with relative deviations within $\pm 13\%$ compared with certified reference values. The results demonstrate the feasibility of implementing reliable radiation instrumentation using commercially available electronic components, providing an accessible solution for environmental, laboratory, and educational monitoring applications. Full article

Equivalent circuit models (ECMs), owing to their simple structure, high computational efficiency, and ease of embedded implementation, have become the most practically applicable modeling approach in lithium-ion battery management systems (BMSs). This paper provides a systematic review of the research progress in lithium-ion battery ECMs along the main line of model construction, parameter identification, and state estimation. First, the topological characteristics, mathematical representations, and application scenarios of the Rint, Thevenin, partnership for a new generation of vehicles (PNGV), dual-polarization, high-order RC, Randles, and fractional-order models are summarized and compared, thereby revealing the inherent trade-off among model accuracy, complexity, and real-time performance. Second, open-circuit voltage–state of charge (OCV–SOC) calibration, offline/online parameter identification, and ECM-based state of charge (SOC) estimation methods are reviewed, with particular emphasis on the advantages and limitations of least squares, recursive least squares, Kalman filtering, particle filtering, sliding-mode observers, and model–data fusion methods. Furthermore, based on model validation and comparative performance results, it is shown that simple models possess high real-time capability but limited dynamic characterization ability; the first-order RC model achieves a more favorable balance between accuracy and complexity; and although high-order models can improve dynamic fitting and state estimation accuracy, they also increase parameter dimensionality and implementation cost. Finally, the key issues faced in this field are distilled, including insufficient adaptability under full operating conditions and across the full lifecycle, inadequate multi-physics coupled modeling, limited integration depth between physical constraints and data-driven methods, and the lack of a unified standardized validation framework. Future research is expected to further advance toward adaptive variable-structure modeling, multi-physics coupling, intelligent hybrid modeling, and unified benchmark testing. This review can provide a systematic reference for ECM design, parameterization method selection, and the development of BMS state estimation strategies for lithium-ion batteries. Full article

A low-cost, high-robust, high-gain preamplifier is fabricated in a 65 nm CMOS for Hall sensors. A semi-open loop that combines an optimized differential–difference amplifier with a simplified open-loop amplifier is proposed to achieve stringent transconductance matching, with the same DC and AC conditions. A two-stage reconfigurable structure, together with an embedded offset-accumulation elimination scheme, relieves open-loop stress and achieves both high fidelity and accurate gain. The experimental results show that the preamplifier has a variable gain of 34.6–46.6 dB and a gain error no more than 0.12% under process/voltage/temperature variations and without error calibration, at the cost of an active area of 0.076 mm² and a current dissipation of 0.7 mA under a 2.5–3.3 V supply. Specifically, both the loop topology and the transconductance match are different from the existing works. Full article

Underwater acoustic monitoring is a critical technology for marine resource development and modern aquaculture. The performance of acoustic sensors directly determines the effectiveness of biological behavior tracking in complex marine environments. This paper presents the design, fabrication, and characterization of a custom hydrophone utilizing a polyvinylidene fluoride (PVDF) piezoelectric film configured in a biomimetic tympanic cavity structure. Operating on the direct piezoelectric effect, the device employs a pre-tensioned PVDF diaphragm integrated with a dedicated charge amplifier circuit to condition high-impedance signals. Laboratory calibrations demonstrate a stable frequency response (with a sensitivity variation within ±1 dB) in the low-frequency range (1–200 Hz) with an average acoustic pressure sensitivity of approximately −206 dB (re 1 V/μPa), providing a higher relative voltage gain compared to a commercial reference hydrophone with a nominal sensitivity of −210 dB (re 1 V/μPa). Furthermore, extensive field evaluations were conducted in a marine net pen to analyze acoustic data across multiple fish feeding scenarios (baseline, pre-feeding, active feeding, and post-feeding). The proposed custom hydrophone exhibited a superior dynamic range and successfully locked onto a 24.4 Hz Golden Pompano (Trachinotus blochii) bioacoustic signature, maintaining remarkable feature stability even after active feeding ceased. This study validates the efficacy of the biomimetic PVDF hydrophone for low-frequency acoustic detection, providing a robust hardware foundation for automated behavioral recognition systems in aquaculture. Full article

A probabilistic data-driven framework for voltage quality forecasting in distribution transformers based on Gaussian process regression and high-resolution field measurements is presented. Voltage time series acquired under real operating conditions were modeled using composite covariance functions designed to capture long-term trends and stochastic multi-scale fluctuations. The proposed approach enables simultaneous prediction and uncertainty quantification, allowing direct compliance assessment with voltage quality standards. The additive Gaussian process models achieved coefficients of determination above 0.75 and produced statistically uncorrelated residuals, indicating an adequate representation of the intrinsic temporal structure. However, the predictive intervals exhibit a certain level of undercoverage, indicating that, while uncertainty is effectively quantified, there is still room for improvement in calibration. The selected kernel structures revealed distinct physical regimes in the voltage dynamics, including smooth steady operation, moderately irregular behavior associated with localized disturbances, and multi-scale stochastic variability. For benchmarking purposes, results were compared with those obtained from a stochastic damped harmonic oscillator with restoring force, a naive model, a seasonal naive model and an Autoregressive Integrated Moving Average model. The oscillator model, the naive model, the seasonal naive model, and the Autoregressive Integrated Moving Average model generated strongly autocorrelated residuals, whereas the Gaussian process models yielded consistent white-noise residuals that outperformed all the other models. These findings demonstrate that probabilistic Gaussian process modeling provides an interpretable, scalable, and uncertainty-aware alternative for predictive voltage quality assessment in modern distribution systems. Full article

Magnetoplasmadynamic thrusters represent a promising Electric Propulsion technology for future space missions; however, their optimization is hampered by the lack of accurate performance models in the emerging regime of low power (<12 kW) and high magnetic fields (>0.1 T), where traditional formulations prove inadequate. In this work, a new semi-empirical model for predicting the thrust and discharge voltage of argon-fed MPD thrusters was developed and validated. Starting from state-of-the-art physical models, multi-factorial correction factors were introduced to account for the coupled effects of discharge current (8–180 A), mass flow rate (3–21 mg/s), and applied magnetic field (up to 0.6 T). The model was calibrated and validated using a comprehensive and homogeneous collection of experimental data from the literature. A comparative analysis demonstrates that the corrected model significantly reduces prediction errors (0–9%) compared to reference models available in the literature (8–50%). In particular, the model exhibits remarkably superior accuracy in both the Self-Field and Applied-Field regimes, overcoming the main limitations of previous formulations and providing more robust estimates across a wide operational envelope. The developed model constitutes a reliable and physically consistent tool for the analysis and preliminary design of low-power, argon-fed magnetoplasmadynamic thrusters, enabling more effective optimization for this class of propulsion systems. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based on the Xilinx Artix-7 FPGA. To overcome attenuation limitations of high-frequency signals, we employ a 4-bit Barker code-encoded excitation scheme coupled with a programmable ±100 V high-voltage transmission circuit. This effectively enhances echo energy without exceeding peak voltage safety thresholds. At the receiver end, the system utilizes a multi-channel analog front end integrated with mixed-signal time-gain compensation technology. Furthermore, to address transmission bottlenecks for massive echo data, we designed a Low-Voltage Differential Signaling (LVDS) interface logic based on dynamic phase calibration, ensuring stable, high-speed data transfer to the host computer via USB 3.0. Experimental results with a 20 MHz transducer demonstrate that the system achieves real-time B-mode imaging at 30 frames per second. Phantom testing revealed an axial resolution of 0.13 mm, enabling clear differentiation of 0.1 mm microstructures. Compared to conventional single-pulse excitation, coded excitation technology improved signal-to-noise ratio (SNR) by approximately 4.5 dB at a depth of 40 mm. These results validate the system’s capability for high-precision deep imaging suitable for clinical endoscopy applications, delivered in a compact, low-power form factor. Full article

Measurement While Drilling (MWD) tools are widely employed in deep and ultra-deep well drilling. In the high-temperature and high-pressure (HTHP) environments characteristic of these wells, structural deformation induced by thermal expansion interferes with the bit parameter recorder’s sensor readings, thereby degrading the measurement accuracy of weight on bit (WOB) and working torque (WT). To address this issue, this paper proposes a temperature compensation method based on thermo-mechanical coupling simulation. This method systematically establishes the quantitative relationships between multiple loads—including WT, WOB, temperature, and make-up torque—and the strain at critical locations of the bit parameter recorder through finite element analysis (FEA). Furthermore, surface calibration experiments have verified a strong linear correlation between the strain gauge voltage signals and the simulated strain. Building upon this foundation, an inversion-based compensation algorithm is developed. This algorithm effectively isolates the interference caused by thermally induced deformation and inversely deduces the true WOB and torque values by utilizing downhole-measured sensor voltage and temperature data. The research results demonstrate that the proposed temperature compensation method significantly improves the measurement accuracy of the bit parameter recorder under harsh, high-temperature operating conditions. The relative errors for both WOB and torque measurements are controlled to within 5%, providing a reliable solution for precise parameter measurement in high-temperature deep wells. Full article

Ultrasonic testing is a prevalent method for non-destructive evaluation of railway rails; however, conventional Time-of-Flight (ToF) approaches applied in practical dry-coupled inspections often rely on simplified assumptions regarding wave propagation velocity and neglect complex waveform characteristics. This paper presents a robust depth estimation framework for surface-breaking cracks that enhances sizing accuracy through effective velocity calibration and Hilbert envelope extraction. Unlike standard methods that assume the free-space speed of sound in air (343 m/s) for wave propagation within the air-filled gap of a surface-breaking crack, we propose an effective velocity model derived from in situ calibration to account for the boundary layer viscosity and thermal conduction effects within narrow crack geometries. The signal processing chain incorporates spectral analysis, band-pass filtering, and Hilbert Transform-based envelope detection to mitigate noise and resolve phase ambiguities. Experimental validation on steel specimens with controlled defects (0.2–10.0 mm) demonstrates that the proposed method achieves an exceptional linear correlation (R² ≈ 0.9976). The calibrated effective velocity was determined to be 289.3 m/s, approximately 15.6% lower than the speed of sound in air, confirming the significant influence of confinement effects. Furthermore, excitation parameters were optimized, identifying that high-voltage excitation (≥110 V) and a tuned pulse width (≈150 ns) are critical for maximizing the signal-to-noise ratio. The results confirm that combining physical model calibration with advanced signal analysis significantly reduces systematic errors, paving the way for portable, high-precision rail inspection systems. Full article

Integrated optical devices based on lithium niobate (LN) are pivotal in modern navigation systems, telecommunications, and sensing technologies. However, their practical implementation is critically limited by temperature-dependent and long-term operational instability, primarily attributed to the pyroelectric effect inherent in LN. This study addresses this challenge by investigating thermally reduced lithium niobate as a material platform to enhance the stability of integrated optical circuits, with a focus on integrated optical electric field sensors (IOES). We present the fabrication and comprehensive characterization of an IOES based on a Michelson interferometer design. Key performance metrics including optical loss, free spectral range, electro-optical sensitivity, and optical path difference were systematically evaluated. Notably, under normal climatic conditions, the optical path difference of the IOES demonstrated exceptional stability when subjected to an applied voltage ranging from 0 to 5 V, with no observable drift over time. Calibration of the IOES revealed a predominantly linear response, although a third-degree polynomial model provided a more precise fit to the experimental data. The minimum relative error achieved during calibration was 0.47%, underscoring the high accuracy of the device. Our results establish thermally reduced LN as a promising material platform for next-generation integrated optical devices. By mitigating the pyroelectric effect, this approach enables significant improvements in the long-term stability of IOES and other LN-based photonic components. These findings open avenues for the reliable deployment of integrated optical systems in demanding applications where environmental stability is paramount. Full article

The damage status of shield machine disc cutters directly impacts the safety and efficiency of tunnelling projects. Current manual inspection methods involve high risks and low efficiency, while existing detection methods suffer from low accuracy and poor real-time performance in complex environments, often lacking quantitative analysis capabilities. To address these issues, this paper proposes an intelligent identification and quantitative assessment method for disc cutter damage based on the Deep Forest (DF) model. First, an eddy current sensor calibration platform was established, and a mapping relationship between output voltage and actual wear was developed through piecewise fitting to achieve precise wear quantification. In the data preprocessing stage, signal quality was improved via filtering, and typical damage features such as edge chipping, cracks, and eccentric wear were extracted using pulse edge detection. These feature segments were then resampled to construct the model training dataset. The DF model utilizes a hierarchical ensemble structure to mine data correlations, enabling accurate identification of four states: normal, edge chipping, eccentric wear, and cracks. Simultaneously, a DF regression model was employed to provide continuous quantitative predictions of damage size. Experimental results show that the classification model achieved accuracies of 98%, 96%, and 96% on the training, validation, and test sets, respectively, with weighted average F1-scores exceeding 0.96. The regression model achieved a coefficient of determination (R²) of 0.9940 and a root mean square error (RMSE) of 0.4051 on the test set. Both models demonstrate excellent performance and generalization, achieving full coverage from “qualitative state identification” to “quantitative wear assessment,” thereby providing reliable decision support for cutter maintenance and replacement. Full article

Accurate electric field measurement in high-voltage direct current (HVDC) environments is essential for power system monitoring. This study systematically investigates the output characteristics of a micro-electro-mechanical system (MEMS) electric field sensor under DC ionized field conditions. Using a controlled experimental platform capable of generating independent nominal electric fields and ion flows, the influence of ion current density on sensor sensitivity and offset was quantitatively analyzed. Experimental results reveal that ion flow leads to a progressive output drift and significant measurement deviations when using conventional electrostatic calibration. To address this issue, a joint calibration method incorporating ion current density is proposed. Validation experiments demonstrate that the proposed method significantly improves measurement accuracy, reducing the maximum relative error from 29.28% to approximately 5.07%. This work provides a reliable experimental basis and calibration methodology for utilizing MEMS electric field sensors in complex ionized DC environments. Full article

Background: Arterial stenosis produces nonlinear changes in vascular impedance that are challenging to investigate in real time using either benchtop flow phantoms or high-fidelity computational fluid dynamics (CFD) models. Objective: This study aimed to develop and evaluate a low-cost printed circuit board (PCB) analog capable of reproducing the hemodynamic effects of progressive arterial stenosis through an R–L–C mapping of vascular mechanics. Methods: A lumped-parameter (0D) electrical network was constructed in which voltage represented pressure, current represented flow, resistance modeled viscous losses, capacitance corresponded to vessel compliance, and inductance represented fluid inertance. A variable resistor simulated focal stenosis and was adjusted incrementally to represent progressive narrowing. Input U_in, output U_out, peak-to-peak V_pp, and mean V_avg voltages were recorded at a driving frequency of 50 Hz. Physiological correspondence was established using the canonical relationships. $R={\displaystyle \frac{8\mu l}{\pi r^4}}$, $L={\displaystyle \frac{pl}{\pi r^2}}$, $C={\displaystyle \frac{3\pi r^3}{2Eh}}$, where μ is blood viscosity, ρ is density, E is Young’s modulus, and h is wall thickness. A calibration constant was applied to convert measured voltage differences into pressure differences. Results: As simulated stenosis increased, the circuit exhibited a monotonic rise in U_out and V_pp, with a precise inflection beyond mid-range narrowing—consistent with the nonlinear growth in pressure loss predicted by fluid dynamic theory. Replicate measurements yielded stable, repeatable traces with no outliers under nominal test conditions. Qualitative trends matched those of surrogate 0D and CFD analyses, showing minimal changes for mild narrowing (≤25%) and a sharp increase in pressure loss for moderate to severe stenoses (≥50%). The PCB analog uses a simplified, lumped-parameter representation driven by a fixed-frequency sinusoidal excitation and therefore does not reproduce fully characterized physiological systolic–diastolic waveforms or heart–arterial coupling. In addition, the present configuration is intended for relatively straight peripheral arterial segments and is not designed to capture the complex geometry and branching of specialized vascular beds (e.g., intracranial circulation) or strongly curved elastic vessels (e.g., the thoracic aorta). Conclusions: The PCB analog successfully reproduces the characteristic hemodynamic signatures of arterial stenosis in real time and at low cost. The model provides a valuable tool for educational and research applications, offering rapid and intuitive visualization of vascular behavior. Current accuracy reflects assumptions of Newtonian, laminar, and lumped flow; future work will refine calibration, quantify uncertainty, and benchmark results against physiological measurements and full CFD simulations. Full article

Tunnel Field-Effect Transistors (TFETs) are being explored for ultra-low-power very-large-scale integrated circuits (VLSI) because their band-to-band tunnelling (BTBT) transport permits subthreshold swings (SS) below the 60 mV/dec thermionic limit at room temperature, along with significantly lower leakage than MOSFETs. This paper presents a systematic TCAD study of DG-TFETs that maps how four primary knobs–gate dielectric materials, silicon channel thickness, temperature variation, and different channel material shape key figures of merit: the ON current (I_ON), OFF current (I_OFF), threshold voltage (V_TH), SS, and the I_ON/I_OFF switching ratio. High-k gate enhances gate-to-channel coupling and boost tunnelling efficiency; rigorous body scaling enhances electrostatic control; and targeted source-proximal doping profiles elevate I_ON while minimizing leakage. We also measure the trade-offs between I_ON, SS, and I_OFF that occur when scaling is performed at the same time. This shows that careful coordination is needed instead of just tuning one parameter. This is a simulated work, and the physical models are calibrated to experimental TFET data and all parameters are checked against previously reported results. The device reaches SS = 31.4 mV/dec, V_TH = 0.46 V, I_ON = 5.91 × 10⁻⁵ A and an I_ON/I_OFF of about 4.5 × 10¹¹. This shows that it can switch quickly with little leakage. The design insights that come from this work provide useful advice regarding how to choose gate dielectric material, structures, and doping strategies to add DG-TFETs to the next generation of low-power semiconductor technologies. Full article