Search Results (611)

Measuring weak magnetic fields proposes significant challenges to the sensing capabilities of magnetic field sensors. The magnetic field detection capacity of tunnel magnetoresistance (TMR) sensors is often insufficient for such applications, necessitating targeted optimization strategies to improve their performance in weak-field measurements. Utilizing magnetic flux concentrators (MFCs) offers an effective approach to enhance TMR sensitivity. In this study, the finite element method was employed to analyze the effects of different MFC geometric structures on the uniformity of the magnetic field in the air gap and the magnetic circuit gain (MCG). It was determined that the MCG of the MFC is not directly related to the absolute values of its parameters but rather to their ratios. Simulation analyses evaluated the impact of these parameter ratios on both the MCG and its spatial distribution uniformity, leading to the formulation of MFC design optimization principles. Building on these simulation-derived principles, several MFCs were fabricated using the 1J85 material, and an experimental platform was established to validate the simulation findings. The fabricated MFCs achieved an MCG of 7.325 times. Based on the previously developed TMR devices, a detection sensitivity of 2.46 nT/$\sqrt{\mathrm{Hz}}$ @1Hz was obtained. By optimizing parameter configurations, this work provides theoretical guidance for further enhancing the performance of TMR sensors in magnetic field measurements. Full article

High-quality reconstruction of magnetic resonance imaging (MRI) data from undersampled k-space remains a significant challenge in medical imaging. While the integration of compressed sensing and deep learning has notably improved the performance of MRI reconstruction, existing convolutional neural network-based methods are limited by their small receptive fields, which hinders the exploration of global image features. Meanwhile, Swin-Transformer-based approaches struggle with inter-window information interaction and global feature extraction and perform poorly when dealing with complex repetitive structures and similar texture features under undersampling conditions, resulting in suboptimal reconstruction quality. To address these issues, we propose a Symmetry-based Cascaded Dual-Domain Hybrid Attention Network (SCDDHAN). Leveraging the inherent symmetry of medical images, the network combines channel and self-attention to improve global context modeling and local detail restoration. The overlapping window self-attention module is designed with symmetry in mind to improve cross-window information interaction by overlapping adjacent windows and directly linking neighboring regions. This facilitates more accurate detail recovery. The concept of symmetry is deeply embedded in the network design, guiding the model to better capture regular patterns and balanced structures within MRI images. Experimental results demonstrate that under 5× and 10× undersampling conditions, SCDDHAN outperforms existing methods in artifact suppression, achieving more natural edge transitions, clearer complex textures and superior overall performance. This study highlights the potential of integrating symmetry concepts into hybrid attention modules for accelerating MRI reconstruction and offers an efficient, innovative solution for future research in this area. Full article

This review article provides an in-depth analysis of recent advancements in the fabrication, structural characterization, and magnetic properties of Heusler alloy glass-coated microwires, focusing on Co₂FeSi alloys. These microwires exhibit unique thermal stability, high Curie temperatures, and tunable magnetic properties, making them suitable for a wide range of applications in spintronics, magnetic sensing, and biomedical engineering. The review emphasizes the influence of geometric parameters, annealing conditions, and compositional variations on the microstructure and magnetic behavior of these materials. Detailed discussions on the Taylor–Ulitovsky fabrication technique, X-ray diffraction (XRD) analysis, and scanning electron microscopy (SEM) provide insights into the structural properties of the microwires. The magnetic properties, including room-temperature behavior, temperature dependence, and the effects of annealing, are thoroughly examined. The potential applications of these microwires in advanced spintronic devices, magnetic sensors, and biomedical technologies are explored. The review concludes with future research directions, highlighting the potential for further advancements in the field of Heusler alloy microwires. Full article

To enhance the sensing performance of fiber-optic magnetic field sensors, we explored the design, optimization, and application prospects of a D-type fiber-optic magnetic field sensor. This D-type PCF-SPR sensor is metal coated on one side (the metal used in this study is gold), which serves as the active metal for SPR and enhances structural stability. Magnetic fluid is applied on the outer side of the gold film for SPR magnetic field sensing. Six internal air holes arranged in a hexagonal shape form a central light transmission channel that facilitates the connection between the two modes, which are the sensor’s core mode and SPP mode, respectively. The outer six large air holes and two small air holes are arranged in a circular pattern to form the cladding, which allows for better energy transmission and reduces energy loss in the fiber. In this paper, the finite element method is employed to analyze the transmission performance of the sensor, focusing on the transmission mode. Guidelines for optimizing the PCF-SPR sensor are derived from analyzing the fiber optic sensor’s dispersion curve, the impact of surface plasmon excitation mode, and the core mode energy on sensing performance. After analyzing and optimizing the transmission mode and structural parameters, the optimized sensor achieves a magnetic field sensitivity of 18,500 pm/mT and a resolution of 54 nT. This performance is several orders of magnitude higher than most other sensors in terms of sensitivity and resolution. The SPR-PCF magnetic field sensor offers highly sensitive and accurate magnetic field measurements and shows promising applications in medical and industrial fields. Full article

Background: The increasing use of technologies operating between 10 and 200 kHz, such as RFID, wireless power transfer systems, and induction cooktops, raises concerns about electromagnetic interference (EMI) with cardiac implantable electronic devices (CIEDs). The mechanisms of interaction within this frequency range have been only partially addressed by both the scientific and regulatory communities. Methods: A physical twin of a pacemaker/implantable defibrillator (PM/ICD) was developed to experimentally assess voltages induced at the input stage by low-to-mid-frequency magnetic fields. The setup simulates the two sensing modalities programmable in PMs/ICDs and allows for the analysis of different implant configurations, lead geometries, and positions within a human body phantom. Results: Characterization of the physical twin demonstrated its capability to reliably measure induced voltages in the range of 5 mV to 1.5 V. Its application enabled the identification of factors beyond the implant’s induction area that contribute to the induced voltage, such as the electrode-tissue interface and body-induced currents. Conclusions: This physical twin represents a valuable tool for experimentally validating the mechanisms of EMI in CIEDs, providing insights beyond current standards. The data obtained can serve as a reference for the validation of numerical models and patient-specific digital twins. Moreover, it offers valuable information to guide future updates and revisions of international electromagnetic compatibility standards for CIEDs. Full article

A dual-parameter optical fiber sensor for measuring the magnetic field and temperature based on the Fabry–Perot interferometer (FPI) and magnetic polymer film was proposed and designed, realizing dual-parameter measurement of temperature and the magnetic field. The sensor uses the excellent elasticity and thermal expansion coefficient of PDMS and the magnetostrictive effect of Fe₃O₄ magnetic polymer film to provide magnetic field and temperature detection while maintaining good reusability, achieving a magnetic field sensitivity and temperature sensitivity of 69 pm/mT and 390 pm/K, respectively. The sensor has the advantages of a low cost, a simple manufacturing process, good linearity, and a sensitive temperature response. It has broad application prospects in medicine, geography, aerospace, and other fields. Full article

Despite ongoing concerns about electromagnetic interference affecting cardiac implantable electronic devices (CIEDs) in the electrical industry workplaces, no study has experimentally assessed induced voltages in CIEDs under exposure to power-frequency magnetic fields. This study addresses this gap by quantifying such interference using a dedicated experimental setup to reproduce high intensity magnetic fields and to measure voltages induced on CIEDs under exposure. A thorough analysis was carried out in comparison with formula-based and simulation approaches applied in previous studies. The induced voltages on CIEDs were measured across varying configurations, including sensing mode, implantation method, exposure frequency, and magnetic field orientation. Our findings reveal the induced voltage levels under exposure from a statistical perspective and highlight correlations between susceptibility and the impact factors, with unipolar configurations and left pectoral implants exhibiting the highest susceptibility. This work provides insights into electromagnetic interference risks for CIED carriers and supports the development of individual protection strategies to enhance occupational safety. Full article

Smart tools are limited by actuation–sensing integration and structural redundancy, making it difficult to achieve compactness, ultra-precision feed, and immediate feedback. This paper proposes a self-sensing giant magnetostrictive actuator-based turning tool (SSGMT), which enables simultaneous actuation and output sensing without external sensors. A multi-objective optimization model is first established to determine the key design parameters of the SSGMT to improve magnetic transfer efficiency, system compactness, and sensing signal quality. Then, a dynamic hysteresis model with a Hammerstein structure is developed to capture its nonlinear characteristics. To ensure accurate positioning and a robust response, a hybrid control strategy combining feedforward compensation and adaptive feedback is implemented. The SSGMT is experimentally validated through a series of tests including self-sensing displacement accuracy and trajectory tracking under various frequencies and temperatures. The prototype achieves nanometer-level resolution, stable output, and precise tracking across different operating conditions. These results confirm the feasibility and effectiveness of integrating actuation and sensing in one structure, providing a promising solution for the application of smart turning tools. Full article

The present study aims to characterize complex geological structures and significant mineralization using remote sensing and aeromagnetic studies. Structural lineaments play a crucial role in the localization and concentration of mineral deposits. For the first time over the study district, a combination of aeromagnetic data, Landsat 9, ASTER, and PRISMA hyperspectral data was utilized to enhance the characterization of both lithological units and structural features. Advanced image processing techniques, including false color composites, principal component analysis (PCA), independent component analysis (ICA), and SMACC, were applied to the remote sensing datasets. These methods enabled effective discrimination between Phanerozoic rock formations and the complex basement units, which comprise the island arc assemblage, Dokhan volcanics, and late-orogenic granites. The local and deep magnetic sources were separated using Gaussian filters. The Neoproterozoic basement rocks were estimated using the radial average power spectrum technique and the Euler deconvolution technique (ED). According to the RAPS technique, the average depths to shallow and deep magnetic sources are approximately 0.4 km and 1.6 km, respectively. The obtained ED contacts range in depth from 0.081 to 1.5 km. The research area revealed massive structural lineaments, particularly in the northeast and northwest sides, where a dense concentration of these lineaments was identified. The locations with the highest densities are thought to signify more fracturization in the rocks that are thought to be connected to mineralization. According to the automatic lineament extraction methods and rose diagram, NW-SE, NNE-SSW, and N-S are the major structural directions. These trends were confirmed and visually represented through textural analysis and drainage pattern control. The lithological mapping results were validated through field observations and petrographic analysis. This integrated approach has proven highly effective, showcasing significant potential for both detailed structural analysis and accurate lithological discrimination, which may be related to further mineralization exploration. Full article

This study introduces a Self-Attention (SA) Generative Adversarial Network (GAN) framework that applies artificial intelligence techniques to microwave sensing for electromagnetic imaging. The approach involves illuminating anisotropic objects using Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves, while sensing antennas collecting the scattered field data. To simplify the training process, a Back Propagation Scheme (BPS) is employed initially to calculate the preliminary permittivity distribution, which is then fed into the GAN with SA for image reconstruction. The proposed GAN with SA offers superior performance and higher resolution compared with GAN, along with enhanced generalization capability. The methodology consists of two main steps. First, TM waves are used to estimate the initial permittivity distribution along the z-direction using BPS. Second, TE waves estimate the x- and y-direction permittivity distribution. The estimated permittivity values are used as inputs to train the GAN with SA. In our study, we add 5% and 20% noise to compare the performance of the GAN with and without SA. Numerical results indicate that the GAN with SA demonstrates higher efficiency and resolution, as well as better generalization capability. Our innovation lies in the successful reconstruction of various uniaxial objects using a generator integrated with a self-attention mechanism, achieving reduced computational time and real-time imaging. Full article

Touch is a crucial sense for advanced organisms, particularly humans, as it provides essential information about the shape, size, and texture of contacting objects. In robotics and automation, the integration of tactile sensors has become increasingly relevant, enabling devices to properly interact with their environment. This study aimed to develop a biomimetic, skin-inspired tactile sensor device capable of sensing applied force, characterizing it in three dimensions, and determining the point of application. The device was designed as a 4 × 4 matrix of tunneling magnetoresistive sensors, which provide a higher sensitivity in comparison to the ones based on the Hall effect, the current standard in tactile sensors. These detect magnetic field changes along a single axis, wire-bonded to a PCB and encapsulated in epoxy. This sensing array detects the magnetic field from an overlayed magnetorheological elastomer composed of Ecoflex and 5 µm neodymium–iron–boron ferromagnetic particles. Structural integrity tests showed that the device could withstand forces above 100 N, with an epoxy coverage of 0.12 mL per sensor chip. A 3D movement stage equipped with an indenting tip and force sensor was used to collect device data, which was then used to train neural network models to predict the contact location and 3D magnitude of the applied force. The magnitude-sensing model was trained on 31,260 data points, being able to accurately characterize force with a mean absolute error ranging between 0.07 and 0.17 N. The spatial sensitivity model was trained on 171,008 points and achieved a mean absolute error of 0.26 mm when predicting the location of applied force within a sensitive area of 25.5 mm × 25.5 mm using sensors spaced 4.5 mm apart. For points outside the testing range, the mean absolute error was 0.63 mm. Full article

In this study, we analysed remote sensing data collected during the Beirut port explosion on 4 August 2020 at 15.08 UT. For this purpose, we selected three Low-Earth-Orbit (LEO) satellite missions that passed near the Beirut port explosion site immediately after the event. The satellites involved were Swarm-B, the Defence Meteorological Satellite Program (DMSP-F17), and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2). This study focused on identifying the possible ionospheric signatures of explosion in both hemispheres. The conjugate hemispheric points were traced using the International Geomagnetic Reference Field (IGRF) model. We found that the satellite data revealed disturbances not only over the explosion site in the Northern Hemisphere, but also in its corresponding conjugate region in the Southern Hemisphere. Ionospheric electron density disturbances were observed poleward in the conjugate hemispheres along the paths of the Swarm and DMSP satellites, whereas the magnetic field data from Swarm-B showed both equatorward and poleward disturbances. Additionally, the ionospheric disturbances detected by Swarm-B (18:52 UT) and DMSP-F17 (16:30 UT) at the same location suggested travelling ionospheric disturbance (TID) oscillations with identical spatial patterns for both satellites, whereas the disturbances observed by COSMIC-2 south of the explosion site (10°N) indicated the radial propagation of TIDs. COSMIC-2 not only recorded equatorward topside (>550 km) ionospheric electron density disturbances, but also in the conjugate hemispheres, which aligns with the time frame reported in previous studies. These ionospheric features observed by multiple LEO satellites indicate that the detected signatures originated from the event, highlighting the importance of integrating space missions for monitoring and gaining deeper insight into space hazards. The absence of equatorward ionospheric disturbances at the altitudes of DMSP-F17 and Swarm-B warrant further investigation. Full article

Thulium iron garnet (Tm₃Fe₅O₁₂, TmIG) is a promising material for next-generation spintronic and quantum technologies owing to its high Curie temperature and strong perpendicular magnetic anisotropy. However, conventional magnetometry techniques are limited by insufficient spatial resolution and sensitivity to probe local magnetic phase transitions and critical spin dynamics in thin films. In this study, we present the first quantitative investigation of local magnetic field fluctuations near the Curie temperature in TmIG thin films using nitrogen-vacancy (NV) center-based quantum sensing. By integrating optically detected magnetic resonance (ODMR) and NV spin relaxometry (T₁ measurements) with macroscopic techniques such as SQUID magnetometry and Hall effect measurements, we systematically characterize both the static magnetization and dynamic spin fluctuations across the magnetic phase transition. Our results reveal a pronounced enhancement in NV spin relaxation rates near 360 K, providing direct evidence of critical spin fluctuations at the nanoscale. This work highlights the unique advantages of NV quantum sensors for investigating dynamic critical phenomena in complex magnetic systems and establishes a versatile, multimodal framework for studying local phase transition kinetics in high-temperature magnetic insulators. Full article

Under the dual impetus of the “Dual Carbon” goals and the construction of smart grids, the development of new energy power infrastructure has been fully realized. The All-Fiber Optical Current Transformer (FOCT), leveraging its unique advantages, is in the process of supplanting traditional current transformers to become the core component of power system monitoring equipment. Currently, to achieve higher precision and stability in magnetic field or current detection, FOCT structures frequently incorporate active components such as Y-waveguides and phase modulators, and closed-loop feedback systems are often used in demodulation. This has led to issues of high cost, complex demodulation, and difficult maintenance, significantly hindering the further advancement of FOCTs. Addressing the problems of high cost and complex demodulation, this paper proposes a passive multiplexing structure that achieves time-domain multiplexing of pulsed sensing signals, designs a corresponding intensity demodulation algorithm, and applies this structure to FOCTs. This enables low-cost, simple-demodulation current sensing, which can also be utilized for magnetic field detection, showcasing vast application potential. Full article

Accurate current sensing in rectangular conductors is challenged by mechanical deformations, including eccentricity (X/Y-axis shifts) and inclination (Z-axis tilt), which distort magnetic field distributions and induce measurement errors. To address this, we propose a bio-inspired error compensation strategy integrating an elliptically configured Hall sensor array with a hybrid Grey Wolf Optimizer (GWO)-enhanced backpropagation neural network. The eccentric displacement and tilt angle of the conductor are quantified via a three-dimensional magnetic field reconstruction and current inversion modeling. A dual-stage optimization framework is implemented: first, establishing a BP neural network for real-time conductor state estimations, and second, leveraging the GWO’s swarm intelligence to refine network weights and thresholds, thereby avoiding local optima and enhancing the robustness against asymmetric field patterns. The experimental validation under extreme mechanical deformations (X/Y-eccentricity: ±8 mm; Z-tilt: ±15°) demonstrates the strategy’s efficacy, achieving a 65.07%, 45.74%, and 76.15% error suppression for X-, Y-, and Z-axis deviations. The elliptical configuration reduces the installation footprint by 72.4% compared with conventional circular sensor arrays while maintaining a robust suppression of eccentricity- and tilt-induced errors, proving critical for space-constrained applications, such as electric vehicle powertrains and miniaturized industrial inverters. This work bridges bio-inspired algorithms and adaptive sensing hardware, offering a systematic solution to mechanical deformation-induced errors in high-density power systems. Full article