This article introduces a novel plasmonic magnetic field sensor (MFS) that utilizes a Metal-Insulator-Metal (MIM) waveguide configuration with a W-shaped cavity filled with magnetic fluid (MF). The ...MFS's unique design combines the advantages of plasmonic sensing, offering a promising solution for the detection of magnetic field strength. It operates based on the inherent properties of surface plasmon polaritons and the magneto-optical properties of MF, resulting in a shift in resonant wavelength. The performance of the proposed MFS has been investigated through numerical calculation employing the finite element method (FEM). Remarkably, the MFS exhibits a maximum magnetic field sensitivity of 49.11 pm/Oe, covering a detection range from 33 Oe to 200 Oe. The recorded figure of merit (FOM) and Q-factor of the MFS are 18.39 and 18.4 respectively, attesting to its high performance and reliability. This innovation has the potential to revolutionize fields such as navigation, medical diagnostics, and robotics technologies by seamlessly integrating optical sensing into traditional devices. The proposed sensor's excellent performance, compact size, and cost-effectiveness position it as a promising technology for widespread adoption, contributing to advancements in magnetic field sensing across scientific, industrial, and technological domains.
After decades of research, there is still no solution for indoor localization like the GNSS (Global Navigation Satellite System) solution for outdoor environments. The major reasons for this ...phenomenon are the complex spatial topology and RF transmission environment. To deal with these problems, an indoor scene constrained method for localization is proposed in this paper, which is inspired by the visual cognition ability of the human brain and the progress in the computer vision field regarding high-level image understanding. Furthermore, a multi-sensor fusion method is implemented on a commercial smartphone including cameras, WiFi and inertial sensors. Compared to former research, the camera on a smartphone is used to "see" which scene the user is in. With this information, a particle filter algorithm constrained by scene information is adopted to determine the final location. For indoor scene recognition, we take advantage of deep learning that has been proven to be highly effective in the computer vision community. For particle filter, both WiFi and magnetic field signals are used to update the weights of particles. Similar to other fingerprinting localization methods, there are two stages in the proposed system, offline training and online localization. In the offline stage, an indoor scene model is trained by Caffe (one of the most popular open source frameworks for deep learning) and a fingerprint database is constructed by user trajectories in different scenes. To reduce the volume requirement of training data for deep learning, a fine-tuned method is adopted for model training. In the online stage, a camera in a smartphone is used to recognize the initial scene. Then a particle filter algorithm is used to fuse the sensor data and determine the final location. To prove the effectiveness of the proposed method, an Android client and a web server are implemented. The Android client is used to collect data and locate a user. The web server is developed for indoor scene model training and communication with an Android client. To evaluate the performance, comparison experiments are conducted and the results demonstrate that a positioning accuracy of 1.32 m at 95% is achievable with the proposed solution. Both positioning accuracy and robustness are enhanced compared to approaches without scene constraint including commercial products such as IndoorAtlas.
Magnetometer data acquired by the MESSENGER spacecraft in orbit about Mercury permit the separation of internal and external magnetic field contributions. The global planetary field is represented as ...a southward-directed, spin-aligned, offset dipole centered on the spin axis. Positions where the cylindrical radial magnetic field component vanishes were used to map the magnetic equator and reveal an offset of 484 ± 11 kilometers northward of the geographic equator. The magnetic axis is tilted by less than 3° from the rotation axis. A magnetopause and tail-current model was defined by using 332 magnetopause crossing locations. Residuals of the net external and offset-dipole fields from observations north of 30°N yield a best-fit planetary moment of 195 ± 10 nanotesla- ${\mathrm{R}}_{\mathrm{M}}^{3}\phantom{\rule{0ex}{0ex}}$ , where R M is Mercury's mean radius.
Finding the interrelation among the magnetic response of the heat mediators to an alternating magnetic field (AMF) and other relevant parameters in magnetic hyperthermia therapy (MHT) can give the ...possibility to accurately design high-performance nanostructured magnetic nanoparticles (MNPs). In this context, the present work investigates the effect of the zinc substitution on magnetic properties and heat-generating ability of poly vinyl alcohol-coated Zn-substituted cobalt ferrite nanoparticles (PZC NPs) with different zinc contents (ZnxCo1−xFe2O4; x = 0, 0.15, 0.3, 0.4, 0.5, 0.7), synthesized using hydrothermal-assisted co-precipitation method. The obtained results showed that the PZC NPs with an average particle size ∼ 15 nm exhibit ferrimagnetic features and their coercivity (Hc) values decrease as the zinc content (x) increases. Moreover, as Zn2+ replaces Co2+ in the structure, saturation magnetization (Ms) increases up to about 52 emu/g for PZC-30 NPs (x =0.3) and then decreases for higher Zn contents. The hyperthermia measurements were performed at a fixed filed frequency (f=120kHz) and different magnetic field strengths (Happl=17,20,24.5kA/m). The results revealed that, as x increases, specific absorption rate (SAR) at each Happl first shows an increasing trend and then reaching a maximum value (at x=0.4) follows a decreasing trend. Moreover, the highest SAR (25.25 W/g) belongs to the magnetic fluid containing PZC-40 NPs at Happl=24.5kA/m. Furthermore, the Happl-dependency of the SAR values were obtained as a power law (SAR∼Happln) in which the exponent n rises as x decreases, suggesting that the optimal composition tends to shift to ones with the lower x at higher Happl. The obtained results, revealing the high impact of the interrelation between the chemical composition and the Happl on the MNPs heating efficiency, can shed more light on the way to design heat mediators with optimal performance through simultaneous control of the chemical composition and the Happl.
This study explores thermal criticality and dissipation involving a two-step reaction in a hyperbolic tangential fluid flow and quadratic Boussinesq approximation to model the complex internal heat ...transfer mechanisms during combustion. Subject to suitable convective boundary conditions, the transformed energy and momentum equations are numerically solved using Galerkin approximation integration coupled with a weighted residual scheme. The outcomes are disseminated using a variety of graphs to illustrate for parametric sensitivities of the thermal and velocity profiles. Based on the results, it is discovered that increases in the Frank-Kamenetskii parameter, Brinkman number, Weissenberg number, activation energy, activation ratio term, and second step term all aid in the complete combustion of hydrocarbons. Monitoring all terms that stimulate internal heat generation is essential to avoid system blow-ups.
The nature and origin of turbulence and magnetic fields in the intergalactic space are important problems that are yet to be understood. We propose a scenario in which turbulent-flow motions are ...induced via the cascade of the vorticity generated at cosmological shocks during the formation of the large-scale structure. The turbulence in turn amplifies weak seed magnetic fields of any origin. Supercomputer simulations show that the turbulence is subsonic inside clusters and groups of galaxies, whereas it is transonic or mildly supersonic in filaments. Based on a turbulence dynamo model, we then estimated that the average magnetic field strength would be a few microgauss (μG) inside clusters and groups, approximately 0.1 μG around clusters and groups, and approximately 10 nanogauss in filaments. Our model presents a physical mechanism that transfers the gravitational energy to the turbulence and magnetic field energies in the large-scale structure of the universe.
Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn ...from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer's disease (AD) classification performance.
T1-weighted brain MRI scans from 151 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer's Coordinating Center (NACC, n = 565) were used for model validation.
The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets.
This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.
The current investigation is to examine the compound impact of electromagnetic induced force and internal heat source on a tangent hyperbolic fluid in quadratic Boussinesq approximation. The current ...hyperbolic tangent liquid flow and heat transport formulation model adequately predicts and characterizes the shear-stricken event. The nonlinear dimensionless heat transfer flow equations are solved completely using weighted residual solution procedures coupled with Galerkin approximation integration approach. The results in the table and graphs revealed that the magnetic field strength has a substantial impact on the fluid flow and heat propagation, as well as the internal heat source. Therefore, the entropy generation is optimized through an enhanced thermodynamic equilibrium and adequate control of heat generating terms and energy loss.
Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. ...Acquiring this additional information requires protocol adaptations and additional scanner time, but is not necessary for the majority of patients who do not have AS. We observed that the relative signal intensity of blood in the ascending aorta on a balanced steady state free precession (bSSFP) 3-chamber cine was often reduced in those with significant aortic stenosis. We investigated whether this effect could be quantified and used to predict AS severity in comparison to existing gold-standard measurements.
Multi-centre, multi-vendor retrospective analysis of patients with AS undergoing CMR and transthoracic echocardiography (TTE). Blood signal intensity was measured in a ∼1 cm2 region of interest (ROI) in the aorta and left ventricle (LV) in the 3-chamber bSSFP cine. Because signal intensity varied across patients and scanner vendors, a ratio of the mean signal intensity in the aorta ROI to the LV ROI (Ao:LV) was used. This ratio was compared using Pearson correlations against TTE parameters of AS severity: aortic valve peak velocity, mean pressure gradient and the dimensionless index. The study also assessed whether field strength (1.5 T vs. 3 T) and patient characteristics (presence of bicuspid aortic valves (BAV), dilated aortic root and low flow states) altered this signal relationship.
314 patients (median age 69 IQR 57–77, 64% male) who had undergone both CMR and TTE were studied; 84 had severe AS, 78 had moderate AS, 66 had mild AS and 86 without AS were studied as a comparator group. The median time between CMR and TTE was 12 weeks (IQR 4–26). The Ao:LV ratio at 1.5 T strongly correlated with peak velocity (r = −0.796, p = 0.001), peak gradient (r = −0.772, p = 0.001) and dimensionless index (r = 0.743, p = 0.001). An Ao:LV ratio of < 0.86 was 84% sensitive and 82% specific for detecting AS of any severity and a ratio of 0.58 was 83% sensitive and 92% specific for severe AS. The ability of Ao:LV ratio to predict AS severity remained for patients with bicuspid aortic valves, dilated aortic root or low indexed stroke volume. The relationship between Ao:LV ratio and AS severity was weaker at 3 T.
The Ao:LV ratio, derived from bSSFP 3-chamber cine images, shows a good correlation with existing measures of AS severity. It demonstrates utility at 1.5 T and offers an easily calculable metric that can be used at the time of scanning or automated to identify on an adaptive basis which patients benefit from dedicated imaging to assess which patients should have additional sequences to assess AS.
Motion artifacts are a common occurrence in Magnetic Resonance Imaging exam. Motion during acquisition has a profound impact on workflow efficiency, often requiring a repeat of sequences. ...Furthermore, motion artifacts may escape notice by technologists, only to be revealed at the time of reading by the radiologists, affecting their diagnostic quality. There is a paucity of clinical tools to identify and quantitatively assess the severity of motion artifacts in MRI. An image with subtle motion may still have diagnostic value, while severe motion may be uninterpretable by radiologists and requires the exam to be repeated. Therefore, a tool for the automatic identification of motion artifacts would aid in maintaining diagnostic quality, while potentially driving workflow efficiencies. Here we aim to quantify the severity of motion artifacts from MRI images using deep learning. Impact of subject movement parameters like displacement and rotation on image quality is also studied. A state-of-the-art, stacked ensemble model was developed to classify motion artifacts into five levels (no motion, slight, mild, moderate and severe) in brain scans. The stacked ensemble model is able to robustly predict rigid-body motion severity across different acquisition parameters, including T1-weighted and T2-weighted slices acquired in different anatomical planes. The ensemble model with XGBoost metalearner achieves 91.6% accuracy, 94.8% area under the curve, 90% Cohen's Kappa, and is observed to be more accurate and robust than the individual base learners.
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