Search Results (9)

Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, solving partial differential equations and systems of linear equations, accelerating machine learning (ML) inference, multi-beam beamforming, signal processing, quantum simulation, and statistical inference. We highlight CMOS implementations that leverage switched-capacitor, switched-current, and radio-frequency circuits, as well as non-CMOS implementations that leverage non-volatile memory, wave physics, and stochastic processes. These advancements demonstrate high-speed, energy-efficient computations for computational electromagnetics, finite-difference time-domain (FDTD) solvers, artificial intelligence (AI) inference engines, wireless systems, and related applications. Theoretical foundations, experimental validations, and potential future applications in high-performance computing and signal processing are also discussed. Full article

The quantitative characterization of near-surface heterogeneity using ground-penetrating radar (GPR) is an important but challenging task. The estimation of subsurface geostatistical parameters from surface-based common-offset GPR reflection data has so far relied upon a Monte-Carlo-type inversion approach. This allows for a comprehensive exploration of the parameter space and provides some measure of uncertainty with regard to the inferred results. However, the associated computational costs are inherently high. To alleviate this problem, we present an alternative deep-learning-based technique, that, once trained in a supervised context, allows us to perform the same task in a highly efficient manner. The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. An important aspect of the training process is that the synthetic GPR data are generated using a computationally efficient approximate solution of the underlying physical problem. This strategy effectively addresses the notorious challenge of insufficient training data, which frequently impedes the application of deep-learning-based methods in applied geophysics. Tests on a wide range of realistic synthetic GPR data generated using a finite-difference time-domain (FDTD) solution of Maxwell’s equations, as well as a comparison with the results of the traditional Monte Carlo approach on a pertinent field dataset, confirm the viability of the proposed method, even in the presence of significant levels of data noise. Our results also demonstrate that typical mismatches between the dominant frequencies of the analyzed and training data can be readily alleviated through simple spectral shifting. Full article

In this article, an analytical solution to a hydraulic network with a wide range of pipe lengths (up to 10 km) is proposed. The Finite-Difference Time-Domain (FDTD) method was applied with the aim of creating a regulation model for controlling both the flow rate of water from one of the two sources and the discharge pressure in the system. The system inertia requires an understanding of boundary conditions in the operation of pipeline networks, which must be known in order to regulate the required parameters with only minor deviations. The proposed model was compared to experimental data, while the mean absolute deviations in the individual system branches ranged from 1 to 5.19%. The created regulation model was subsequently tested by applying linear, sine and stochastic changes in the output load, while the ability to control the discharge pressure and the selected water flow rate was analysed. The effect of coefficient ε, which multiplies the effect of the difference between the measured and the predicted value of the discharge pressure on the boundary conditions of the discharge pressure in the system, was analysed in this paper. With the use of the proposed unsteady simulation of the fluid flow in the hydraulic system arranged in parallel and in series, the maximum deviation of the regulated pressure was 1.2% and the maximum deviation of the regulated flow rate was 5.3%. Full article

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies have been conducted on the relationship between the amplitude of ETS and the topsoil layer’s electromagnetic parameters (dielectric permittivity and conductivity) through numerical simulations, laboratory experiments, and field experiments. Soil is a typical inhomogeneous medium, and soil cultivation is a factor affecting its heterogeneity. In this context, we discuss the ability of the amplitude of ETS to estimate the water content of heterogeneous soil. First, we establish a multi-scale stochastic medium model with the inhomogeneous distribution of dielectric permittivity and conductivity and simulate the GPR response by the finite-difference time-domain (FDTD) method to observe the influence of medium heterogeneity on the GPR response. The heterogeneity of the soil models is evaluated by a geostatistical analysis described by two parameters, correlation length and variability. Then, we analyze the relationship between variability and the average envelope amplitude (AEA) of ETS. A strong soil heterogeneity increases the error of the AEA method in estimating SWC. Finally, the AEA method is used to estimate the SWC of two adjacent fields with different heterogeneities, which were caused by different cultivation methods. The results of the numerical simulation and field experiment indicate that the soil heterogeneity can have an impact on the estimation of SWC using EST, with an error lower than 3% within a depth range of 1/2 λ to λ (wavelength). This suggests that the EST of GPR can be applied to soil layers with relatively large lateral changes in water content. Full article

Electromagnetic (EM) scattering may be a significant source of degradation in signal and power integrity of high-contrast silicon-on-insulator (SOI) nano-scale interconnects, such as opto-electronic or optical interconnects operating at 100 s of THz where two-dimensional (2D) analytical models of dielectric slab waveguides are often used to approximate scattering loss. In this work, a formulation is presented to relate the scattering (propagation) loss to the scattering parameters (S-parameters) for the smooth waveguide; the results are correlated with results from the finite-difference time-domain (FDTD) method in 2D space. We propose a normalization factor to the previous 2D analytical formulation for the stochastic scattering loss based on physical parameters of waveguides exhibiting random surface roughness under the exponential autocorrelation function (ACF), and validate the results by comparing against numerical experiments via the 2D FDTD method, through simulation of hundreds of rough waveguides; additionally, results are compared to other 2D analytical and previous 3D experimental results. The FDTD environment is described and validated by comparing results of the smooth waveguide against analytical solutions for wave impedance, propagation constant, and S-parameters. Results show that the FDTD model is in agreement with the analytical solution for the smooth waveguide and is a reasonable approximation of the stochastic scattering loss for the rough waveguide. Full article

In the paper, the author deals with modeling the stochastic behavior of ordinary patch antennas in terms of the mean and standard deviation of their reflection coefficient |S₁₁| under the geometrical uncertainty associated with their manufacturing process. The Artificial Neural Network is used to model the stochastic reflection coefficient of the antennas. The Polynomial Chaos Expansion and FDTD computations are used to obtain the training and testing data for the Artificial Neural Network. For the first time, the author uses his analytical transformations to reduce the required number of highly time-consuming FDTD simulations for a given set of nominal values of the design parameters of the ordinary patch antenna. An analysis is performed for n257 and n258 frequency bands (24.5–28.7 GHz). The probability distributions of the design parameters are extracted from the measurement results obtained for a series of manufactured patch antenna arrays for three different frequencies in the C, X, and Ka bands. Patch antennas are chosen as the subject of the scientific analysis in this paper because of the popularity of the patch antennas in the scientific literature concerning antennas, as well as because of a simple form of these antennas that is reflected in the time required for computation of training and testing data for the Artificial Neural Network. Full article

On January 3rd 2019, the Chang’e-4 mission successfully landed in the Von Kármán Crater inside the South Pole-Aitken (SPA) basin and achieved the first soft landing on the farside of the Moon. Lunar penetrating radar (LPR) equipped on the rover measured the shallow subsurface structure along the motion path for more than 700 m. LPR data could be used to obtain the dielectric properties of the materials beneath the exploration area, providing important clues as to the composition and source of the materials. Although the properties of the upper fine-grained regolith have been studied using various methods, the underlying coarse-grained materials still lack investigation. Therefore, this paper intends to estimate the loss tangent of the coarse-grained materials at depth ranges of ~12 and ~28 m. Stochastic media models with different rock distributions for the LPR finite-difference time-domain (FDTD) simulation are built to evaluate the feasibility of the estimation method. Our results show that the average loss tangent value of coarse-grained materials is $0.0104\pm 0.0027$, and the abundance of $\mathrm{F}\mathrm{e}{\mathrm{O}}_{\mathrm{T}}+\mathrm{Ti}{\mathrm{O}}_2 \mathrm{is} 20.08 \mathrm{wt}.\%$, which is much higher than the overlying fine-grained regolith, indicating different sources. Full article

The uncertainties present in a variety of electromagnetic (EM) problems may have important effects on the output parameters of interest. Unfortunately, deterministic schemes are not applicable in such cases, as they only utilize the nominal value of each random variable. In this work, a two-dimensional (2D) finite-difference time-domain (FDTD) algorithm is presented, which is suitable for assessing randomness in the electrical properties, as well as in the dimensions of orthogonal objects. The proposed technique is based on the stochastic FDTD method and manages to extract the mean and the standard deviation of the involved field quantities in one realization. This approach is applied to three test cases, where uncertainty exists in the electrical and geometrical parameters of various materials. The numerical results demonstrate the validity of our scheme, as similar outcomes are extracted compared to the Monte Carlo (MC) algorithm. Full article

The propagation characteristics of high-power laser beams in plasma is an important research topic and has many potential applications in fields such as laser machining, laser-driven accelerators and laser-driven inertial confined fusion. The dynamic evolution of high-power Laguerre-Gaussian (LG) beams in plasma is numerically investigated by using the finite-difference time-domain (FDTD) method based on the nonlinear Drude model, with both plasma frequency and collision frequency modulated by the light intensity of laser beam. The numerical algorithms and implementation techniques of FDTD method are presented for numerically simulating the nonlinear permittivity model of plasma and generating the LG beams with predefined parameters. The simulation results show that the plasma has different field modulation effects on the two exemplified LG beams with different cross-sectional patterns. The self-focusing and stochastic absorption phenomena of high-power laser beam in plasma are also demonstrated. This research also provides a new means for the field modulation of laser beams by plasma. Full article