Search Results (431)

Accurate and non-invasive monitoring of glucose levels remains a critical challenge in diabetes management, motivating the development of highly sensitive optical biosensors. In this work, a surface plasmon resonance-based biosensor operating in the Kretschmann configuration is proposed and numerically investigated for glucose detection. The sensor architecture consists of a BK7 prism/TiO₂/Ag/graphene multilayer, and the effect of incorporating a hexagonal boron nitride (h-BN) interlayer with varying thicknesses is systematically analyzed to enhance sensing performance. Electromagnetic simulations were performed using the finite-difference time-domain method in Lumerical FDTD Solutions at a wavelength of 633 nm. Key performance parameters, including angular sensitivity, full width at half maximum, detection accuracy, figure of merit, signal-to-noise ratio, and limit of detection, were evaluated for glucose concentrations corresponding to refractive indices ranging from 1.3282 to 1.3767 RIU. The conventional BK7/TiO₂/Ag/TiO₂/Graphene/Sensing Medium (SM) configuration achieved a sensitivity of 167.48 deg/RIU. By introducing an h-BN layer, significant performance enhancement was observed. The optimized structure with an 8 nm h-BN layer exhibited a maximum angular sensitivity of 205.35 deg/RIU, representing an improvement of approximately 22.6% over the reference design, while maintaining a low detection limit of 2.43 × 10⁻⁴ RIU. The results further reveal that h-BN thickness plays a crucial role in balancing sensitivity and resonance quality, where excessive thickness broadens the resonance curve and degrades detection accuracy. The proposed graphene-h-BN-assisted SPR platform demonstrates high potential for high-performance, non-invasive glucose monitoring and provides practical design guidelines for next-generation plasmonic biosensors. Full article

In this study, we propose a novel optical sensor architecture based on two-dimensional photonic crystals for the early detection of cervical cancer (HeLa). The structure consists of a central hollow-disk micro-cavity designed to accommodate biosamples, surrounded by a periodic array of GaAs rods. The detection principle relies on variations in the biosample refractive index, inducing a spectral shift in the resonance. To overcome the limitations of conventional 2D-FDTD method parametric sweeps, an artificial intelligence framework was developed to optimize the geometric parameters of the proposed photonic crystal optical sensor. First, a Random Forest algorithm was employed to identify promising regions of the geometric design space. Next, a multilayer artificial neural network (ANN-MLP) was trained as a high-fidelity surrogate model ($R^2$ = 98.58%) and coupled with a Particle Swarm Optimization (PSO) algorithm to determine the optimal structural configuration. The optimized sensor geometry subsequently achieved an average sensitivity of 5512.91 nm/RIU, a quality factor of 6139.15 and a detection limit of $5.64\times {10}^{-5}$ RIU, demonstrating the effectiveness of the proposed AI-assisted design strategy. The optimized design reduces classical performance trade-offs and exhibits high tolerance to nanometric fabrication deviations below ±20 nm. Full article

Terahertz (THz) waves offer unique advantages, including non-contact operation, high penetration capability, and high resolution, making them particularly well-suited for the non-destructive thickness measurement of film-structured materials. In reflective terahertz time-domain spectroscopy (THz-TDS), thickness measurement approaches are generally classified into three categories: optimization-based methods that rely on theoretical models, time-of-flight (ToF), and machine learning. Model-based optimization techniques require precise knowledge of the optical parameters and structural configuration of each layer; however, they often suffer from slow convergence and are prone to becoming trapped in local optima. In contrast, ToF-based methods determine thickness by calculating the time delay between echo pulses reflected from different interfaces, yet their applicability is limited when the film thickness is extremely small. Machine learning, especially deep learning, enables the establishment of a direct, data-driven mapping between THz waveforms (or their extracted features) and the target thickness. Such approaches offer rapid inference, strong robustness to noise, and good adaptability to thin or structurally complex films, although their accuracy remains dependent on the quality of training data and the generalization capability of the model. In this study, high-fidelity THz waveform data generated via finite-difference time-domain (FDTD) simulations are utilized to conduct a comparative investigation into the film thickness prediction performance of several representative machine learning algorithms, including Back Propagation (BP) neural networks, Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), K-Nearest Neighbors (KNN), and Partial Least Squares (PLS) regression. The results indicate that, in terms of prediction error, the overall ranking of algorithmic performance from best to worst is: PLS > RF > SVM > BP > ELM > KNN. These findings provide valuable guidance for the future application of machine learning-assisted THz-TDS in precise film thickness measurement. Full article

This paper presents a structured review of antenna and electromagnetic spectrum monitoring techniques for non-invasive fault detection in electrical and electronic equipment. Electromagnetic emissions from partial discharges, arc faults, insulation degradation, and component ageing carry diagnostic signatures detectable through remote radio-frequency sensing. We review (1) antenna technologies spanning magnetic-field loops to ultra-high-frequency electric-field sensors, including fractal, Vivaldi, spiral, and bio-inspired designs; (2) data acquisition platforms ranging from laboratory oscilloscopes to software-defined radio receivers and IoT edge nodes; (3) signal processing methods including time–frequency analysis, adaptive decomposition, and statistical techniques; and (4) machine learning approaches from classical classifiers to deep learning architectures such as convolutional neural networks, recurrent neural networks, and Transformer-based models. Unlike prior surveys focusing on individual fault types or specific equipment classes, this review connects all five layers of the sensing pipeline—from electromagnetic emission physics through antenna selection, signal acquisition, processing, and intelligent classification—for partial-discharge, arc, and insulation faults and analyses the cross-layer constraints that couple them. Design optimisation techniques based on computational electromagnetic methods (FDTD, FEM) and sensitivity calibration challenges are discussed. Open challenges, including the lack of standardised UHF calibration, cross-equipment generalisation, and the scarcity of open electromagnetic fault datasets, are identified, along with emerging directions in flexible antennas, edge AI, and digital twin integration. Full article

The convex blazed grating is a key dispersive component in high-performance spectrometers, offering advantages such as a broad operating wavelength range, uniform dispersion, high diffraction efficiency, and the ability to achieve a large field of view. With the popularization of spectral detection technology and the ever-increasing demand for specialization, its design and fabrication technologies have drawn considerable attention in the field. This paper systematically reviews the development history of convex blazed grating design theory, from early scalar diffraction theory to the current mainstream rigorous vector methods, including rigorous coupled-wave analysis (RCWA), the finite-difference time-domain (FDTD) method, and commercial software such as Gsolver and PCGrate, and summarizes the applicable scenarios and limitations of each method. In terms of fabrication techniques, we comprehensively survey three typical technology routes—mechanical ruling, holographic ion beam etching, and electron beam lithography—covering their principles and progress, and analyze their respective merits and drawbacks in terms of precision, operating waveband, groove profile flexibility, and production capacity through comparative analysis. On this basis, we highlight recent breakthroughs achieved via electron beam lithography in blaze angle control and high-aspect-ratio etching for convex blazed gratings spanning from the ultraviolet to the very-long-wave infrared band; the diffraction efficiency has exceeded 80%, and such gratings have been successfully applied in aerospace engineering projects. Finally, this paper summarizes the current challenges facing convex blazed grating technology and provides an outlook on future development trends, including fabrication uniformity on curved substrates, large-area high-precision manufacturing, and design–process co-optimization, with the aim of offering a systematic reference for researchers and engineers in related fields. Full article

This paper describes a three-dimensional simulation of electron transmission through a nanoscale transistor using the finite-difference time-domain (FDTD) method. The simulation begins by defining a wave packet that represents an electron at the transistor’s source, followed by modeling the electron’s interaction as it traverses the transistor’s channel to the drain. The software tools employed in this study implement a fully three-dimensional solution to the time-dependent Schrödinger equation, using finite-difference approximations for both temporal and spatial derivatives. The analysis of electron transmission enables the generation of current-voltage (I–V) characteristics under various gate and drain-source voltage conditions. Although the simulations focus on the dynamics of a single electron, the methodology can be extended to accommodate multi-electron systems through the incorporation of density functional methods. Full article

In this paper, a simple and compact 1 × 2 decoder based on a two-dimensional photonic crystal structure is proposed, whose operation relies on total internal reflection and photonic band gaps. The designed structure employs a square-lattice configuration of silicon dielectric rods embedded in air. The decoder consists of two input ports, one acting as a Bias port and the other as a logical input port. Numerical modeling and simulations are performed using the plane-wave expansion (PWE) method and the finite-difference time-domain (FDTD) technique. The proposed coupling-resonator structure increases the coupling efficiency at resonant frequencies. The structure has a relatively small footprint, comprising an 18 × 18 array of dielectric rods with a total area of approximately 147 µm². A minimum contrast ratio of about 8.4 dB between logical “1” and “0” states is achieved. The decoder operates at 1.55 µm, making it suitable for photonic and optical communication applications. Due to its compact size, simple architecture, and use of a minimal number of ring resonators, the proposed decoder is well suited for high-speed photonic integrated circuits and future all-optical computing systems. The bit rate of the proposed decoder is estimated to be 2 Tb/s. Full article

This paper presents the design of an inverted-F antenna intended for integration into a smartwatch operating in the 2.4 GHz band. The antenna design addresses spatial constraints imposed by the device’s miniaturized form factor and the proximity of electronic components, including the printed circuit board, display, and battery. The influence of the user’s body on the antenna’s performance characteristics was considered during the design phase through numerical simulations employing the Finite-Difference Time-Domain (FDTD) method with a heterogeneous human body model. Simulation results and measurements of a fabricated prototype antenna are presented, demonstrating satisfactory performance in terms of impedance matching with VSWR below 1.5 in the whole band and gain of −1 dBi. Full article

Low-voltage distribution networks are critical for supplying power to end-users, and electric shock safety is a key concern; however, the frequent incompleteness of topology information in practical operations makes it challenging to accurately assess electric shock risks. This paper proposes a two-stage approach for electric shock simulation and risk assessment in low-voltage distribution networks with completely unknown topology and absent phase-angle measurements, addressing the critical challenge of unavailable, incomplete, or outdated topology information using only conventional smart meter data. It innovatively investigates shock risks under TT, TN-C, and TN-S grounding systems without prior topology knowledge or synchronized phasors. The proposed methodology combines a phase-angle-agnostic data-driven stage and a model-driven stage: the data-driven stage uses an iterative algorithm for topology label matrix estimation and weighted Laplacian matrix reconstruction with hierarchical clustering to identify network structure and line parameters, requiring only active power, reactive power, voltage magnitude, and current magnitude. The model-driven stage adopts modified nodal analysis with the finite-difference time-domain (MNA-FDTD) method to evaluate transient leakage voltage distribution under single-phase-to-ground faults, thereby assessing electric shock risks in line with international safety standards. Key contributions include a practical phase-free topology identification framework, comparative risk analysis of three grounding systems, and an integrated data-model approach for real-world low-observability networks. Simulation results show accurate topology/parameter identification with a relative Frobenius-norm error of only 1.8% even without phase data. TN-S provides the highest safety complying with IEC standards, followed by TN-C and TT under specific conditions, offering a practical solution for utilities lacking detailed topology records. Full article

In microwave tomography (MWT), reconstruction accuracy is challenged by modeling error, namely the mismatch between the numerical representation and the actual experiment. Accurate antenna modeling is perceived as an important step toward reducing this error, but the actual benefit of increasing antenna model complexity has not been analyzed in the literature. This work fills this gap by conducting a rigorous numerical analysis of the issue using two popular algorithms for its study: the finite-difference time-domain (FDTD) method for antenna and forward-problem modeling, and the distorted Born iterative method (DBIM) for implementing the iterative inversion algorithm. We consider various FDTD tools of increasing complexity to improve the agreement between the FDTD forward solver and an accurate numerical model implemented in commercial software. After validating these models for different antennas, we perform reconstructions for a stroke-detection scenario. Our results show that in a practical setting, sophisticated antenna modeling in the forward solver does not necessarily improve reconstruction accuracy for monopole-type antennas widely used in MWT. Our model-error analysis confirms that calibration is always necessary in practice and that its impact supersedes efforts to model the antenna more faithfully. Full article

Rare-earth-doped microsphere cavities have attracted significant interest for applications in miniaturized photonic devices due to their unique optical properties. In this work, Yb³⁺/Er³⁺ co-doped microsphere cavities were fabricated via a melting method, which enables uniform interior doping at high and tunable rare-earth concentrations through a simpler and more cost-effective process compared with existing coating and fiber-etching approaches. Whispering gallery modes (WGMs) enhanced upconversion luminescence, which was observed using tapered fiber coupling, producing a vivid green fluorescence ring near the equatorial region of the microsphere. The luminescence characteristics of the microsphere cavity were investigated by measuring the fluorescence spectra under varying excitation powers. The results indicated that the fluorescence emission follows a two-photon absorption process, consistent with the upconversion emission mechanism of Er³⁺. A finite difference time domain (FDTD) model was employed to simulate the optical field distribution within the microsphere cavity. At a microsphere diameter of 90 μm and a coupling gap of 0 μm, both the 980 nm pump light and the emitted light were effectively confined near the equatorial region of the microsphere, forming WGM confinement patterns. These findings are expected to advance the application of rare-earth-doped microsphere cavities in fields such as biosensing, bioimaging, optical communications, and upconversion microlasers. Full article

Modern turbine engines, when operating at high temperatures, can inhale calcium–magnesium–alumina–silicate particles (CaO-MgO-Al₂O₃-SiO₂, CMAS) from the air, which can erode the thermal barrier coatings on the blade surface, affecting the service life of the thermal barrier coatings and, in severe cases, leading to premature blade failure. Therefore, it is of great significance to effectively detect the thickness of CMAS deposited on the surface of the thermal barrier coatings at an early stage of CMAS erosion to ensure the high-temperature structural integrity of the hot-end components of aeroengines. Based on this, this study proposes a method combining terahertz time-domain spectroscopy technology and a hybrid machine learning algorithm for the quantitative detection of the thickness of CMAS on the surface of thermal barrier coatings. Firstly, the terahertz time-domain spectroscopy experimental data of CMAS were obtained using a terahertz experimental system, and the refractive index and absorption coefficient of CMAS in the terahertz frequency band were calculated. The FDTD method, Gaussian noise addition, and wavelet denoising processing were combined to further simulate the terahertz detection process of thermal barrier coatings with different thicknesses of CMAS attached to the surface under high-temperature conditions, and the terahertz simulation detection data were obtained. Principal component analysis (PCA) was used to reduce the dimensionality of the original experimental and simulation data, and a support vector machine (SVM) model integrating PCA and bacterial foraging optimization (BFO) algorithm was constructed. The research results show that the integrated model exhibits excellent performance in predicting the thickness of CMAS, with a correlation coefficient of 0.95, and the mean absolute error (MAE) and root mean square error (RMSE) are 0.13 μm and 0.46 μm, respectively. This study provides a new high-precision method for non-destructive detection of the thickness of CMAS on the surface of thermal barrier coatings, which has certain engineering application value for ensuring the service performance of thermal barrier coatings under harsh service conditions. Although the current method is based on simulated and experimental data under controlled conditions, it has the potential to be developed into an in situ monitoring strategy in the future, enabling real-time assessment of CMAS thickness on the coating surface during engine operation. Full article

Electron density is a fundamental parameter characterizing the ionosphere. Multiple ground-based and space-based detection technologies are applied to detect ionospheric electron density using artificial electromagnetic waves, based on the ionospheric effects of reflection, refraction, incoherent scattering, and doppler shift on radio waves. Lightning-generated whistlers (LGWs) constitute a natural signal with a wide spatiotemporal distribution that can substitute for these artificial transmissions, achieving global ionospheric detection. This paper proposes a method for reconstructing ionospheric electron density profiles by comparing simulated and observed dispersion of LGWs. We develop an LGW propagation model based on the finite-difference time-domain (FDTD) algorithm, where the background electron density is derived from the International Reference Ionosphere (IRI) model. The dispersion of simulated whistlers is compared with satellite observations, and a modification factor is introduced to modify the background electron density based on the relationship between dispersion and electron density. The approach is applied to two events, and the electron density modification effect is assessed with independent data sources. The results show that the errors between the modified electron density and the true value in two events are reduced by 62.81% and 69.29%, respectively, confirming the efficacy of the proposed method. Full article

This study presents a method to retrieve the electron density structure of the transient C-region using very-low-frequency (VLF) Earth–ionosphere waveguide propagation. Here, we demonstrate the identification of the C-region from amplitude variations of a mid-range VLF propagation path that is nearly perpendicular to the solar terminator. Previous investigations have primarily relied on phase measurements along long-distance paths with small terminator angles, whereas the present approach utilizes amplitude information under conditions where modal interference is significant. The Faraday International Reference Ionosphere (FIRI-2018) provides an effective semi-empirical model of the lower-ionospheric electron density; however, discrepancies between simulations and observations are often observed at sunrise. To resolve this issue, we introduce Gaussian perturbations to the electron density profile output by FIRI-2018 and optimize their parameters so that finite-difference time-domain (FDTD) simulations reproduce the observed VLF amplitude. The analysis is performed for the 22.2 kHz JJI transmitter signal received in Chofu, Japan over a mid-range propagation path, ∼900 km. The optimized electron density profile successfully reproduces the characteristic features of the C-region, including a temporary enhancement near 65 km altitude during sunrise. These results demonstrate that mid-range VLF amplitude analysis provides a quantitative tool for identifying transient lower- ionospheric structures. Full article

This study presents a computational, simulation-based investigation of the dielectric response of human hand tissues, skin, fat, muscle, and bone to radiofrequency (RF) electromagnetic fields emitted by mobile devices. The widespread adoption of handheld devices and the deployment of fifth-generation (5G) networks, including millimetre-wave (mmWave) bands, have intensified concerns regarding localized human exposure to RF radiation, particularly in the hand, which serves as the primary interface during device operation. Using validated dielectric property datasets, numerical simulations were performed across the frequency range of 0.5–40 GHz, employing the Finite-Difference Time-Domain (FDTD) method to solve Maxwell’s equations, with analytical evaluations conducted in Maple-18. A heterogeneous multilayer hand phantom was developed, and simulations were conducted under controlled exposure conditions, including a transmitted power of 1 W, antenna gain of 2 dBi, and incident power density of 5 W/m², consistent with ICNIRP and NCC safety guidelines. Tissue responses were assessed over a temperature range of 10–40 °C to account for thermal variability. The results demonstrate strong frequency- and temperature-dependent behaviour of dielectric properties, intrinsic impedance, reflection coefficient, attenuation, and specific absorption rate (SAR). At lower frequencies (<1 GHz), RF energy penetrated more deeply with distributed absorption and relatively low SAR values, whereas higher frequencies (3–40 GHz) produced highly localized absorption in superficial tissues, particularly skin and muscle. Increasing temperature led to significant increases in permittivity, conductivity, and SAR, with up to a twofold enhancement observed between 10 °C and 40 °C. These findings confirm that 5G and mmWave exposures result in predominantly surface-confined energy deposition in hand tissues. The study provides a robust computational framework for evaluating hand device electromagnetic interactions and offers quantitative insights relevant to antenna design, exposure compliance assessment, and the development of evidence-based safety guidelines. Full article