Efficient Triangular Interpolation Method: Error Analysis and Applications

The interpolation errors of bivariate Lagrange polynomial and triangular interpolations are studied for the plane waves. The maximum and root-mean-square (RMS) errors on the right triangular, equilateral triangular and rectangular (bivariate Lagrange polynomial) interpolations are analyzed. It is found that the maximum and RMS errors are directly proportional to the (p+1)’th power of kh for both one-dimensional (1D) and two-dimensional (2D, bivariate) interpolations, where k is the wavenumber and h is the mesh size. The interpolation regions for the right triangular, equilateral triangular and rectangular interpolations are selected based on the regions with smallest errors. The triangular and rectangular interpolations are applied to evaluate the 2D singly periodic Green’s function (PGF). The numerical results show that the equilateral triangular interpolation is the most accurate interpolation method, while the right triangular interpolation is the most efficient interpolation method

An Efficient Multilevel Fast Multipole Algorithm to Solve Volume Integral Equation for Arbitrary Inhomogeneous Bi-Anisotropic Objects

A volume integral equation (VIE) based on the mixed-potential representation is presented to analyze the electromagnetic scattering from objects involving inhomogeneous bi-anisotropic materials. By discretizing the objects using tetrahedrons on which the commonly used Schaubert-Wilton-Glisson (SWG) basis functions are defined, the matrix equation is derived using the method of moments (MoM) combined with the Galerkin’s testing. Further, adopting an integral strategy of tetrahedron-to-tetrahedron scheme, the multilevel fast multipole algorithm (MLFMA) is proposed to accelerate the iterative solution, which is further improved by using the spherical harmonics expansion with a faster implementation and low memory requirement. The memory requirement of the radiation patterns of basis functions in the proposed MLFMA is several times less than that in the conventional MLFMA

A Wideband and Polarization-Independent Metasurface Based on Phase Optimization for Monostatic and Bistatic Radar Cross Section Reduction

A broadband and polarization-independent metasurface is analyzed and designed for both monostatic and bistatic radar cross section (RCS) reduction in this paper. Metasurfaces are composed of two types of electromagnetic band-gap (EBG) lattice, which is a subarray with “0” or “” phase responses, arranged in periodic and aperiodic fashions. A new mechanism is proposed for manipulating electromagnetic (EM) scattering and realizing the best reduction of monostatic and bistatic RCS by redirecting EM energy to more directions through controlling the wavefront of EMwave reflected from the metasurface. Scattering characteristics of two kinds of metasurfaces, periodic arrangement and optimized phase layout, are studied in detail. Optimizing phase layout through particle swarm optimization (PSO) together with far field pattern prediction can produce a lot of scattering lobes, leading to a great reduction of bistatic RCS. For the designed metasurface based on optimal phase layout, a bandwidth of more than 80% is achieved at the normal incidence for the −9.5 dB RCS reduction for both monostatic and bistatic. Bistatic RCS reduction at frequency points with exactly 180∘ phase difference reaches 17.6 dB. Both TE and TM polarizations for oblique incidence are considered. The measured results are in good agreement with the corresponding simulations

Network-Based Methods for Prediction of Drug-Target Interactions

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology

Miniaturized-Element Frequency-Selective Rasorber Design Using Characteristic Modes Analysis

A dual-polarization frequency-selective rasorber with two absorptive bands at both sides of a passband is presented. Based on the characteristic mode analysis, a circuit analog absorber is designed using a lossy FSS that consists of miniaturized meander lines and lumped resistors. The positions and values of resistors are determined according to the analysis of modal significances and modal current. After that, the presented rasorber is designed by cascading of the lossy FSS and a lossless bandpass FSS. Equivalent circuits of the frequency-selective rasorber are modelled, and surface current distributions of both FSSs are illustrated to explain the operation mechanism. Measurement results show that, under the normal incidence, a minimum insertion loss of 0.27 dB is achieved at a passband around 6 GHz, and the absorption bands with an absorption rate higher than 80% are 2.5 to 4.6 GHz in the lower band and 7.7 to 12 GHz in the higher band, respectively. Our results exhibit good agreements between measurements and simulations

Uneven-Layered Coding Metamaterial Tile for Ultrawideband RCS Reduction and Diffuse Scattering

In this paper, a novel uneven-layered coding metamaterial tile is proposed for ultra-wideband radar cross section (RCS) reduction and diffuse scattering. The metamaterial tile is composed of two kinds of square ring unit cells with different layer thickness. The reflection phase difference of 180° (±37°) between two unit cells covers an ultra-wide frequency range. Due to the phase cancellation between two unit cells, the metamaterial tile has the scattering pattern of four strong lobes deviating from normal direction. The metamaterial tile and its 90-degree rotation can be encoded as the ‘0’ and ‘1’ elements to cover an object, and diffuse scattering pattern can be realized by optimizing phase distribution, leading to reductions of the monostatic and bi-static RCSs simultaneously. The metamaterial tile can achieve −10 dB RCS reduction from 6.2 GHz to 25.7 GHz with the ratio bandwidth of 4.15:1 at normal incidence. The measured and simulated results are in good agreement and validate the proposed uneven-layered coding metamaterial tile can greatly expanding the bandwidth for RCS reduction and diffuse scattering

Metasurface base on uneven layered fractal elements for ultra-wideband RCS reduction

A novel metasurface based on uneven layered fractal elements is designed and fabricated for ultra-wideband radar cross section (RCS) reduction in this paper. The proposed metasurface consists of two fractal subwavelength elements with different layer thickness. The reflection phase difference of 180◦ (±37◦) between two unit cells covers an ultra-wide frequency range. Ultra-wideband RCS reduction results from the phase cancellation between two local waves produced by these two unit cells. The diffuse scattering of electromagnetic (EM) waves is caused by the randomized phase distribution, leading to a low monostatic and bistatic RCS simultaneously. This metasurface can achieve -10dB RCS reduction in an ultra-wide frequency range from 6.6 to 23.9 GHz with a ratio bandwidth (fH/fL) of 3.62:1 under normal incidences for both x- and y-polarized waves. Both the simulation and the measurement results are consistent to verify this excellent RCS reduction performance of the proposed metasurface

Ultra-wideband, Wide Angle and Polarization-insensitive Specular Reflection Reduction by Metasurface based on Parameteradjustable Meta-Atoms

In this paper, an ultra-wideband, wide angle and polarization-insensitive metasurface is designed, fabricated, and characterized for suppressing the specular electromagnetic wave reflection or backward radar cross section (RCS). Square ring structure is chosen as the basic meta-atoms. A new physical mechanism based on size adjustment of the basic meta-atoms is proposed for ultra-wideband manipulation of electromagnetic (EM) waves. Based on hybrid array pattern synthesis (APS) and particle swarm optimization (PSO) algorithm, the selection and distribution of the basic meta-atoms are optimized simultaneously to obtain the ultra-wideband diffusion scattering patterns. The metasurface can achieve an excellent RCS reduction in an ultra-wide frequency range under x- and y-polarized normal incidences. The new proposed mechanism greatly extends the bandwidth of RCS reduction. The simulation and experiment results show the metasurface can achieve ultra-wideband and polarization insensitive specular reflection reduction for both normal and wide-angle incidences. The proposed methodology opens up a new route for realizing ultra-wideband diffusion scattering of EM wave, which is important for stealth and other microwave applications in the future

Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition

Automatic recognition of disordered speech remains a highly challenging task to date. The underlying neuro-motor conditions, often compounded with co-occurring physical disabilities, lead to the difficulty in collecting large quantities of impaired speech required for ASR system development. This paper presents novel variational auto-encoder generative adversarial network (VAE-GAN) based personalized disordered speech augmentation approaches that simultaneously learn to encode, generate and discriminate synthesized impaired speech. Separate latent features are derived to learn dysarthric speech characteristics and phoneme context representations. Self-supervised pre-trained Wav2vec 2.0 embedding features are also incorporated. Experiments conducted on the UASpeech corpus suggest the proposed adversarial data augmentation approach consistently outperformed the baseline speed perturbation and non-VAE GAN augmentation methods with trained hybrid TDNN and End-to-end Conformer systems. After LHUC speaker adaptation, the best system using VAE-GAN based augmentation produced an overall WER of 27.78% on the UASpeech test set of 16 dysarthric speakers, and the lowest published WER of 57.31% on the subset of speakers with "Very Low" intelligibility.Comment: Submitted to ICASSP 202

Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation

Automatic recognition of dysarthric speech remains a highly challenging task to date. Neuro-motor conditions and co-occurring physical disabilities create difficulty in large-scale data collection for ASR system development. Adapting SSL pre-trained ASR models to limited dysarthric speech via data-intensive parameter fine-tuning leads to poor generalization. To this end, this paper presents an extensive comparative study of various data augmentation approaches to improve the robustness of pre-trained ASR model fine-tuning to dysarthric speech. These include: a) conventional speaker-independent perturbation of impaired speech; b) speaker-dependent speed perturbation, or GAN-based adversarial perturbation of normal, control speech based on their time alignment against parallel dysarthric speech; c) novel Spectral basis GAN-based adversarial data augmentation operating on non-parallel data. Experiments conducted on the UASpeech corpus suggest GAN-based data augmentation consistently outperforms fine-tuned Wav2vec2.0 and HuBERT models using no data augmentation and speed perturbation across different data expansion operating points by statistically significant word error rate (WER) reductions up to 2.01% and 0.96% absolute (9.03% and 4.63% relative) respectively on the UASpeech test set of 16 dysarthric speakers. After cross-system outputs rescoring, the best system produced the lowest published WER of 16.53% (46.47% on very low intelligibility) on UASpeech.Comment: To appear at IEEE ICASSP 202