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Parallel Solution of Integral Equation-Based EM Problems in the Frequency Domain
A step-by-step guide to parallelizing cem codes The future of computational electromagnetics is changing drastically as the new generation of computer chips evolves from single-core to multi-core. The burden now falls on software programmers to revamp existing codes and add new functionality to enable computational codes to run efficiently on this new generation of multi-core CPUs. In this book, you'll learn everything you need to know to deal with multi-core advances in chip design by employing highly efficient parallel electromagnetic code. Focusing only on the Method of Moments (MoM), the book covers: In-Core and Out-of-Core LU Factorization for Solving a Matrix Equation A Parallel MoM Code Using RWG Basis Functions and ScaLAPACK-Based In-Core and Out-of-Core Solvers A Parallel MoM Code Using Higher-Order Basis Functions and ScaLAPACK-Based In-Core and Out-of-Core Solvers Turning the Performance of a Parallel Integral Equation Solver Refinement of the Solution Using the Conjugate Gradient Method A Parallel MoM Code Using Higher-Order Basis Functions and Plapack-Based In-Core and Out-of-Core Solvers Applications of the Parallel Frequency Domain Integral Equation Solver Appendices are provided with detailed information on the various computer platforms used for computation; a demo shows you how to compile ScaLAPACK and PLAPACK on the Windows® operating system; and a demo parallel source code is available to solve the 2D electromagnetic scattering problems. Parallel Solution of Integral Equation-Based EM Problems in the Frequency Domain is indispensable reading for computational code designers, computational electromagnetics researchers, graduate students, and anyone working with CEM software.
eBook
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.
Journal Article
Modification of graphene by polypyrrole and ionic liquids for dual-band electromagnetic interference shielding hydrogels
The most previous research aimed to electromagnetic interference shielding effectiveness (EMI SE) was focused on X-band, which were normally used in communications for satellites. Nowadays, the growth of terahertz technology for 6G communication transfigures mobile and information acquisition. However, few researches have broadcasted the fabrication of hydrogels with application in dual-band EMI SE in X-band and terahertz band (THz-band). Herein, polyvinyl alcohol (PVA) hydrogels applied to dual-band electromagnetic interference (EMI) shielding were prepared by a facile approach. Firstly, reduced graphene oxide (rGO) were modified by polypyrrole (PPy) and ionic liquids (IL) on its nano-sheets to increase the conductivity. Then, the modified rGO (I-P-rGO) was incorporated into PVA solution to in situ form the heterogeneous conducting network in the deformable PVA hydrogels. The obtained PVA/I-P-rGO composite hydrogels exhibited high EMI SE of 32.9 dB in X-band and 22.4 dB in THz-band with 10.0 wt% fillers content due to the heterogeneous conducting network architecture, which opens an avenue for hydrogels applied in shielding electromagnetic waves in X-band and THz-band band at the same time.
Journal Article
An all-dielectric metasurface as a broadband optical frequency mixer
A frequency mixer is a nonlinear device that combines electromagnetic waves to create waves at new frequencies. Mixers are ubiquitous components in modern radio-frequency technology and microwave signal processing. The development of versatile frequency mixers for optical frequencies remains challenging: such devices generally rely on weak nonlinear optical processes and, thus, must satisfy phase-matching conditions. Here we utilize a GaAs-based dielectric metasurface to demonstrate an optical frequency mixer that concurrently generates eleven new frequencies spanning the ultraviolet to near-infrared. The even and odd order nonlinearities of GaAs enable our observation of second-harmonic, third-harmonic, and fourth-harmonic generation, sum-frequency generation, two-photon absorption-induced photoluminescence, four-wave mixing and six-wave mixing. The simultaneous occurrence of these seven nonlinear processes is assisted by the combined effects of strong intrinsic material nonlinearities, enhanced electromagnetic fields, and relaxed phase-matching requirements. Such ultracompact optical mixers may enable a plethora of applications in biology, chemistry, sensing, communications, and quantum optics.
Frequency mixers are hard to achieve at optical frequencies because it is difficult to meet different phase-matching conditions. Here, the authors show that GaAs metasurfaces can mix laser beams to generate eleven new wavelengths through different nonlinear optical processes occurring simultaneously.
Journal Article
Single-layer spatial analog meta-processor for imaging processing
Computational meta-optics brings a twist on the accelerating hardware with the benefits of ultrafast speed, ultra-low power consumption, and parallel information processing in versatile applications. Recent advent of metasurfaces have enabled the full manipulation of electromagnetic waves within subwavelength scales, promising the multifunctional, high-throughput, compact and flat optical processors. In this trend, metasurfaces with nonlocality or multi-layer structures are proposed to perform analog optical computations based on Green’s function or Fourier transform, intrinsically constrained by limited operations or large footprints/volume. Here, we showcase a Fourier-based metaprocessor to impart customized highly flexible transfer functions for analog computing upon our single-layer Huygens’ metasurface. Basic mathematical operations, including differentiation and cross-correlation, are performed by directly modulating complex wavefronts in spatial Fourier domain, facilitating edge detection and pattern recognition of various image processing. Our work substantiates an ultracompact and powerful kernel processor, which could find important applications for optical analog computing and image processing.
Here, the authors propose a Fourier-based metaprocessor to impart customized highly flexible transfer functions for analog computing. Differentiation and cross-correlation are performed to substantiate the ultracompact and high-throughput kernel processor.
Journal Article
Surface Waves on Self-Complementary Metasurfaces: All-Frequency Hyperbolicity, Extreme Canalization, and TE-TM Polarization Degeneracy
Self-complementary metasurfaces have gained significant attention due to their unique frequency-independent transmission and reflection properties and the possibility of the polarization transformation of plane waves. In this paper, we focus on the near-field spectrum to investigate, both theoretically and experimentally, the properties of surface waves supported by anisotropic self-complementary metasurfaces. We show that as a consequence of the electromagnetic Babinet’s duality, such a structure is hyperbolic for any frequency. We demonstrate the possibility of switching the canalization direction of surface waves with ultimately flat phase fronts for 90° by a very small frequency shift, paving the way to the extreme tunability and surface-wave routing. We reveal the polarization degree of freedom inherent to plane waves by demonstrating the all-frequency TE-TM polarization degeneracy of the surface waves along two principal directions. The results obtained open a plethora of opportunities for practical applications, including flat polarization devices, optical data-processing systems, sensing, holography and antennas.
Journal Article
Overview of Hyperspectral Image Classification
With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.
Journal Article
Computation of Analytical Derivatives for Airborne TEM Inversion Using a Cole–Cole Parameterization Based on the Current Waveform of the Transmitter
Airborne transient electromagnetic (ATEM) technology is a technique often used in mineral exploration and geological mapping. Due to inductive polarization (IP) phenomena, the ATEM response curve often shows a negative response or declines rapidly to the attenuation curve. Traditional resistivity inversion techniques do not explain the IP response of a signal well, so the negative response is usually removed during data processing, resulting in a reduced correctness and authenticity of the findings. In this paper, in the parameter inversion based on the Cole–Cole model, the Jacobian matrix chain analysis method is used to calculate, and the current waveform calculation is also considered in the inversion. The results show that compared with the perturbation method, the analysis technique can greatly reduce the calculation time and improve the inversion efficiency. In the single-point one-dimensional inversion and lateral constraint quasi-two-dimensional inversion, the Cole–Cole four-parameter forward response has strong inversion accuracy, which can successfully invert the actual exploration content and the Cole–Cole four-parameter response. Some measured sounding data in the Qingchengzi survey area of Liaoning Province, China have a negative response to IP, and the resistivity scheme cannot be used alone for inversion, but the real underground resistivity structure can be obtained through the method studied in this paper, and good exploration results can be obtained.
Journal Article
Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection.
Journal Article