Search Results (225)

This paper presents a pre-industrial, laboratory-stage version of an innovative sensor box designed to enable remote measurement of electrical currents. The proposed prototype functions as a drone-mounted payload that can be deployed onto overhead transmission lines. Utilizing Hall-effect sensors, electronic signal processing through filtering, and digital data transmission via Arduino and Bluetooth, the instantaneous line currents are visualized in MATLAB (R2023a) as time-based curves. The sensor box can also be remotely released from the transmission line once measurements are complete, allowing a fully autonomous mode of operation. Laboratory tests demonstrated promising results for real-world applications, with measurement efficiencies ranging from 92% to 98% under various test conditions, including stress tests involving harmonics and total harmonic distortion up to 40%. Future work will focus on implementing effective shielding against high electric fields to further enhance reliability and advance the sensor’s industrialization as a novel solution for power grid digitalization. Full article

This study presents a comprehensive performance analysis of four different control strategies, Proportional–Integral (PI), classical Sliding Mode Control (SMC), Super-Twisting SMC (ST-SMC), and Exponential Reaching Law SMC (ERL-SMC), applied to the speed regulation of a Hall-effect sensored Brushless DC (BLDC) motor. A mathematically detailed BLDC motor model, three-phase inverter structure with safe commutation logic, and a high-frequency PWM switching scheme were implemented in the MATLAB/Simulink-2024a environment to provide a realistic simulation framework. The control strategies were evaluated under multiple test scenarios, including variations in supply voltage, mechanical load disturbances, reference speed transitions, and steady-state operation. The comparative results reveal that the classical SMC and PI controllers suffer from significant oscillations, overshoot, and limited disturbance rejection capability, especially during voltage and load transients. The ST-SMC algorithm improves robustness and reduces the chattering effect inherent to first-order SMC but still exhibits noticeable oscillations near the sliding surface. In contrast, the proposed ERL-SMC controller demonstrates superior performance across all scenarios, achieving the lowest steady-state ripple, the shortest settling time, and the most stable transition response while significantly mitigating chattering. These results indicate that ERL-SMC is the most effective and reliable control strategy among the evaluated methods for BLDC speed regulation, which requires high dynamic response and disturbance robustness. The findings of this study contribute to the advancement of SMC-based BLDC motor control, providing a solid foundation for future research that integrates observer-based schemes, adaptive tuning, or real-time hardware implementation. Full article

Understanding the behavioral strategies that allow freshwater mussels to persist under environmental stress is essential for their conservation, yet burrowing behavior remains poorly quantified. We tested whether valve movement data could be used to detect and characterize burrowing in the endangered Westralunio carteri; a species endemic to a region undergoing severe climatic drying. Mussels from multiple populations were monitored individually under laboratory conditions using Hall effect sensors, and valve movement patterns were analyzed to distinguish between burrowing and non-burrowing behaviors. Burrowing was associated with rapid, high-amplitude valve movements that lengthened as burial progressed, while non-burrowing behaviors showed distinct, slower patterns. These differences indicate that valvometry can reliably identify burrowing behavior, providing a non-invasive method for monitoring mussel activity. This approach has broad applications for ecological research, conservation assessment, and early-warning biomonitoring of imperiled freshwater mussel populations. Full article

Accurate seed singulation is critical for uniform crop establishment and yield optimization in precision agriculture. This study presents the development and evaluation of a Hall sensor-based Seed Miss Prevention System (SMPS) integrated into a long-belt finger-clip precision metering device for corn (‘Dekalb DKC5032’) and sunflower (‘Astana’). The system utilizes neodymium magnets mounted on seed-picking fingers to trigger a Hall sensor that detects missed seeds in real time and initiates immediate compensation. Laboratory tests across rotational speeds from 10 to 80 rpm showed that the SMPS significantly reduced seed misses, especially within the 10–30 rpm range, where near-perfect singulation was achieved for corn (Miss Index < 0.01). For sunflower, although performance at very low speeds was limited by mechanical variability, the SMPS effectively reduced the miss index by up to 50 % at medium speeds. Statistical analysis (Tukey HSD) confirmed significant improvements in single and double miss prevention across both crops. The proposed Hall sensor-based approach demonstrated a robust, cost-effective, and dust-resistant solution for enhancing seed placement accuracy, providing a strong foundation for the development of intelligent and adaptive precision seeding systems. Full article

This study presents an experimental framework for mapping the air-gap magnetic flux in electric machines operating under controlled eccentricity and tilt conditions. A six-degree-of-freedom industrial robotic arm positions the rotor, while the stator accommodates a dense single-axis Hall-sensor array. Synchronous data acquisition at 10 kHz captures magnetic-field dynamics during torque-producing excitation. A coordinate-transformation method synthesises virtual rotor poses from a limited set of physical measurements, eliminating the need for exhaustive mechanical scanning. The proposed approach generates pose-resolved RMS and THD maps, together with harmonic amplitude and phase signatures, thereby revealing localised asymmetries and phase-decoherence effects that are not predicted by idealised finite-element models. In a custom PMSM-like prototype, the local RMS value doubles (from 31 mT to 64 mT), while the THD increases by more than 25% across displacement and tilt grids. These findings provide quantitative experimental evidence of misalignment-induced magnetic-field symmetry breaking, supporting model validation and digital-twin calibration for traction, aerospace, and robotic applications. Full article

Flexible magnetic tactile sensors hold great promise for wearable electronics and intelligent robotics but often suffer from limited strain range and complex magnetic field variations due to rigid-soft coupling between the Hall sensor and magnetic layer. In this study, we propose a Halbach-array-based magnetic tactile sensor that structurally decouples the soft magnetic deformation layer from the rigid Hall sensing unit. The sensor embeds k = 2 Halbach-configured magnetic cubes within a PDMS matrix, while the Hall element is fixed at a remote, rigid location. Numerical analysis using COMSOL Multiphysics demonstrates that the Halbach configuration enhances magnetic field strength and uniformity, achieving mT-level detection even at a distance of 15 mm. Moreover, the Halbach array effectively reduces the field distribution from three-dimensional to one-dimensional, enabling stronger directionality, simplified data processing, and higher sensing frequency. This work establishes a theoretical framework for wide-range, high-precision magnetic tactile sensing through magnetic field tailoring, providing valuable guidance for the design of next-generation flexible sensors for wearable, robotic, and embodied intelligence applications. Full article

The growing adoption of renewable energy conversion systems and smart infrastructures has increased the demand for accurate monitoring solutions to ensure system performance and reliability, as well as seamless integration with cloud-based platforms. Voltage and current sensing are central to this task; however, sensor selection often involves a trade-off between cost and measurement precision. Rather than comparing technologies as equivalent options, this study investigates the practical impact of using low-cost versus high-precision sensors in electrical power generation monitoring. The evaluation includes representative low-cost sensors and high-precision alternatives based on instrumentation amplifiers and a closed-loop Hall-effect transducer. All sensors were characterized under controlled laboratory conditions and analyzed using statistical indicators, including MAE, RMSE, MAPE, and R². Results show that high-precision sensors achieved R² > 0.97 and MAPE < 4%, whereas low-cost sensors showed R² as low as 0.73 and errors exceeding 10% under dynamic irradiance conditions. Low-cost sensors present deviations of 5–8% in RMS measurement, while high-precision sensors maintain error below 1%. Full article

An increase in the temperature of the magnetic core causes narrowing of its hysteresis loop and reduction in the saturation magnetic flux density. Therefore, at the same operating point on the magnetization characteristic, the nonlinear effect may become stronger. In the case of the inductive current transformers, this may result in change in their transformation accuracy and increased self-generation of the low-order higher harmonics to the secondary current. Consequently, the equivalent methods used to determine their values of current error and phase displacement without operating conditions resulting from the presence of the secondary current provide less reliable results, which is particularly important for inductive current transformers with high transformation accuracy requirements and may also be significant in certain borderline cases when determining its accuracy class and the value of error is close to the limit. However, ambient temperature does not affect the transformation accuracy of conventional inductive current transformers, as their internal operating temperature is solely driven by the relatively high RMS values of the rated secondary current (1 A or 5 A) and the large number of secondary winding turns evenly distributed over the magnetic core. During thermal testing of a current transducer operating in a closed-loop feedback configuration with a Hall sensor, a deterioration of its conversion accuracy was observed at high ambient temperatures. This was caused primarily by the thermal expansion of the magnetic core, which leads to a change in the dimensions of the air gap where the Hall sensor is placed, and thus also to a change in the electrical parameters of the feedback loop circuit. Full article

This paper presents an electromagnetic levitation system that stabilizes a magnetic body using an array of electromagnets controlled by a Hall-effect sensor array and TinyML-based position detection. Departing from conventional optical tracking methods, the proposed design combines finite-element-optimized electromagnets with a microcontroller-optimized neural network that processes sensor data to predict the levitated object’s position with 0.0263–0.0381 mm mean absolute error. The system employs both quantized and full-precision implementations of a supervised multi-output regression model trained on spatially sampled data (40 × 40 × 15 mm volume at 5 mm intervals). Comprehensive benchmarking demonstrates stable operation at 850–1000 Hz control frequencies, matching optical systems’ performance while eliminating their cost and complexity. The integrated solution performs real-time position detection and current calculation entirely on-board, requiring no external tracking devices or high-performance computing. By achieving sub 30 μm accuracy with standard microcontrollers and minimal hardware, this work validates machine learning as a viable alternative to optical position detection in magnetic levitation systems, reducing implementation barriers for research and industrial applications. The complete system design, including electromagnetic array characterization, neural network architecture selection, and real-time implementation challenges, is presented alongside performance comparisons with conventional approaches. Full article

The degradation of the electronic properties of semiconductor materials and electronic devices under neutron irradiation is a critical issue for the development of electronic systems intended for use in nuclear and thermonuclear energy facilities. This study presents a methodology for real-time measurement of the electrical parameters of semiconductor structures during neutron irradiation in a high-flux reactor environment. A specially designed irradiation fixture with an electrical measurement system was developed and implemented at the WWR-K research reactor. The system enables simultaneous measurement of electrical conductivity and the Hall effect, with automatic temperature control and remote data acquisition. The sealed fixture, equipped with radiation-resistant wiring and a temperature control, allows for continuous measurement of remote material properties at neutron fluences exceeding 10¹⁸ cm⁻², eliminating the limitations associated with post-irradiation handling of radioactive samples. The technique was successfully applied to the two different InGaAs-based heterostructures, revealing distinct mechanisms of radiation-induced modification: degradation of mobility and carrier concentration in the InGaAs quantum well structure on GaAs substrate, and transmutation-induced doping effects in the heterostructure on InP substrate. The developed methodology provides a reliable platform for evaluating radiation resistance and optimizing materials for magnetic sensors and electronic components designed for high-radiation environments. Full article

This study constructed an integrated underwater pipeline monitoring system, which combines pipeline posture sensing modules and pipeline leakage detection modules. The proposed system can achieve the real-time monitoring of pipeline posture and the comprehensive assessment of pipeline damage. By deploying pipeline posture sensing and leakage detection modules in array configurations along an underwater pipeline, information related to pipeline posture and flow variations is continuously collected. An array of inertial sensor nodes that form the pipeline posture sensing system is used for real-time pipeline posture monitoring. The system measures underwater motion signals and obtains bending and buckling postures using posture algorithms. Pipeline leakage is evaluated using flow and water temperature data from Hall sensors deployed at each node, assessing pipeline health while estimating the location and area of pipeline damage based on the flow values along the nodes. The human–machine interface designed in this study for underwater pipelines supports automated monitoring and alert functions, so as to provide early warnings for pipeline postures and the analysis of damage locations before water supply abnormalities occur in the pipelines. Underwater experiments validated that this system can precisely capture real-time postures and damage locations of pipelines using sensing modules. By taking flow changes at these locations into consideration, the damage area with an error margin was estimated. In the experiments, the damage areas were 8.04 cm² to 25.96 cm², the estimated results were close to the actual area trends (R² = 0.9425), and the area error was within 5.16 cm² (with an error percentage ranging from −20% to 26%). The findings of this study contribute to the management efficiency of underwater pipelines, enabling more timely maintenance while effectively reducing the risk of water supply interruption due to pipeline damage. Full article

This study examines the effects of laser pulse duration on the structural, morphological, optical, and gas-sensing characteristics of ${\mathrm{Al}}_2{\mathrm{O}}_3$/AgO thin films deposited on glass substrates using pulsed laser deposition (PLD). Pulse durations of 10, 8, and 6 nanoseconds were achieved through optical lens modifications to control both energy density and laser spot size. X-ray diffraction (XRD) and atomic force microscopy (AFM) analyses showed a distinct reduction in both crystallite and grain sizes with decreasing pulse width, along with significant improvements in surface morphology refinement and film compactness. Hall effect measurements revealed a transition from n-type to p-type conductivity with decreasing pulse width, demonstrating increased hole concentration and reduced carrier mobility attributed to grain boundary scattering. Furthermore, current-voltage (I-V) characteristics demonstrated improved photoconductivity under illumination, with the most pronounced enhancement observed in samples prepared using longer pulse durations. Gas sensing measurements for ${\mathrm{NO}}_2$ and ${\mathrm{H}}_2$S revealed enhanced sensitivity, improved response/recovery characteristics at 250 °C, with optimal performance achieved in films deposited using shorter pulse durations. This improvement is attributed to their larger surface area and higher density of active adsorption sites. Our results demonstrate a clear relationship between laser pulse parameters and the functional properties of ${\mathrm{Al}}_2{\mathrm{O}}_3$/AgO films, providing valuable insights for optimizing deposition processes to develop advanced gas sensors. Full article

The Hall effect is widely used in magnetic field sensors and contactless measurement systems. Accurate modeling of Hall-effect elements is essential for optimizing performance, especially in high-sensitivity applications under controlled conditions like vacuum. This paper introduces a graphical software tool for calculating key electrical parameters of Hall elements, such as Hall voltage, Hall coefficient, and carrier mobility. Users can input variables like semiconductor thickness, current, and magnetic field, with built-in models for materials like silicon, germanium, and gallium arsenide. Designed for vacuum operation, the tool supports simulation-based analysis, aiding researchers and educators in understanding and evaluating Hall-effect devices. Full article

The photonic spin Hall effect (PSHE) has emerged as a powerful metrological approach for precision measurements. Dynamic manipulation of PSHE through external stimuli could substantially expand its applications. In this work, we present a simple and active modulation scheme for PSHE in a surface plasmon resonance (SPR) structure by exploiting electric-field-tunable refractive indices of electro-optic materials. By applying an electric field, the enhancement of PSHE spin shifts is observed, and the dual-field control can further amplify these spin shifts through synergistic effects in this SPR structure. Notably, various operation modes of external electric field enable the real-time switching between two high-performance sensing functionalities (refractive index detection and angle measurement). Therefore, our designed PSHE sensor based on SPR structure with a simple structure of only three layers not only makes up for the complex structure in multi-functional sensors, but more importantly, this platform establishes a new paradigm for dynamic PSHE manipulation while paving the way for advanced multi-functional optical sensing technology. Full article

Real-time acquisition of high-precision 3D spatial information is critical for intelligent maintenance of urban infrastructure. While SLAM technology based on LiDAR–IMU sensor fusion has become a core approach for infrastructure monitoring, its accuracy remains limited by vertical pose estimation drift. To address this challenge, this paper proposes a LiDAR–IMU fusion SLAM algorithm incorporating a dynamic coplanarity constraint and a joint observation model within an improved error-state Kalman filter framework. A threshold-driven ground segmentation method is developed to robustly extract planar features in structured environments, enabling dynamic activation of ground constraints to suppress vertical drift. Extensive experiments on a self-collected long-corridor dataset and the public M2DGR dataset demonstrate that the proposed method significantly improves pose estimation accuracy. In structured environments, the method reduces z-axis endpoint errors by 85.8% compared with Fast-LIO2, achieving an average z-axis RMSE of 0.0104 m. On the M2DGR Hall04 sequence, the algorithm attains a z-axis RMSE of 0.007 m, outperforming four mainstream LiDAR-based SLAM methods. These results validate the proposed approach as an effective solution for high-precision 3D mapping in infrastructure monitoring applications. Full article