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In this work, we propose a deep learning-based imaging method for addressing the multi-frequency electromagnetic (EM) inverse scattering problem (ISP). By combining deep learning technology with EM physical laws, we have successfully developed a multi-frequency neural Born iterative method (NeuralBIM), guided by the principles of the single-frequency NeuralBIM. This method integrates multitask learning techniques with NeuralBIM's efficient iterative inversion process to construct a robust multi-frequency Born iterative inversion model. During training, the model employs a multitask learning approach guided by homoscedastic uncertainty to adaptively allocate the weights of each frequency's data. Additionally, an unsupervised learning method, constrained by the physical laws of ISP, is used to train the multi-frequency NeuralBIM model, eliminating the need for contrast and total field data. The effectiveness of the multi-frequency NeuralBIM is validated through synthetic and experimental data, demonstrating improvements in accuracy and computational efficiency for solving ISP. Moreover, this method exhibits strong generalization capabilities and noise resistance. The multi-frequency NeuralBIM method explores a novel inversion method for multi-frequency EM data and provides an effective solution for the electromagnetic ISP of multi-frequency data.

Modeling of multiple-scattering channels in atmospheric turbulence is essential for the performance analysis of long-distance non-line-of-sight (NLOS) ultraviolet (UV) communications. Existing works on the turbulent channel modeling for NLOS UV communications either ignored the turbulence-induced scattering effect or erroneously estimated the turbulent fluctuation effect, resulting in a contradiction with reported experiments. In this paper, we establish a comprehensive multiple-scattering turbulent channel model for NLOS UV communications considering both the turbulence-induced scattering effect and the turbulent fluctuation effect. We first derive the turbulent scattering coefficient and turbulent phase scattering function based on the Booker-Gordon turbulent power spectral density model. Then an improved estimation method is proposed for both the turbulent fluctuation and the turbulent fading coefficient based on the Monte-Carlo integration approach. Numerical results demonstrate that the turbulence-induced scattering effect can always be ignored for typical UV communication scenarios. Besides, the turbulent fluctuation will increase as either the communication distance, the elevation angle, or the divergence angle increases, which is compatible with existing experimental results. Moreover, we find that the probability density of the equivalent turbulent fading for multiple-scattering turbulent channels can be approximated as a Gaussian distribution., Comment: 29 pages,6 figures

Sepsis requires urgent diagnosis, but research is predominantly focused on Western datasets. In this study, we perform a comparative analysis of two ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis reveals the effectiveness of these methods in addressing healthcare data imbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight edge in computational efficiency and scalability. The study paves the way for the broader application of machine learning in critical care, thereby expanding the reach of predictive analytics in healthcare globally.

Spin-orbit interaction (SOI) offers a nonferromagnetic scheme to realize spin polarization through utilizing an electric field. Electrically tunable SOI through electrostatic gates have been investigated, however, the relatively weak and volatile tunability limit its practical applications in spintronics. Here, we demonstrate the nonvolatile electric-field control of SOI via constructing ferroelectric Rashba architectures, i.e., 2D Bi2O2Se/PMN-PT ferroelectric field effect transistors. The experimentally observed weak antilocalization (WAL) cusp in Bi2O2Se films implies the Rashba-type SOI that arises from asymmetric confinement potential. Significantly, taking advantage of the switchable ferroelectric polarization, the WAL-to-weak localization (WL) transition trend reveals the competition between spin relaxation and dephasing process, and the variation of carrier density leads to a reversible and nonvolatile modulation of spin relaxation time and spin splitting energy of Bi2O2Se films by this ferroelectric gating. Our work provides a scheme to achieve nonvolatile control of Rashba SOI with the utilization of ferroelectric remanent polarization.

In the study of nonlinear inverse scattering problems (ISPs), the traditional method for estimating the number of degrees of freedom (NDoF) of the scattered field does not consider the effect of the scatterer contrast. In this work, we study the relationship between NDoF and scatterer contrast by modifying the radiation matrix. Specifically, three numerical methods for calculating the NDoF are presented. The first NDoF is defined as the number of principle singular values of the external radiation matrix, which is used as a benchmark. The second NDoF is defined based on the modified radiation matrix that incorporates the contrast of domain of investigation (DoI) with the external radiation matrix. By exploring the relationship between the second NDoF and contrast, an approximate upper bound of the second NDoF is obtained, that is, the third NDoF. The accuracy of the proposed NDoF is verified by interpolation experiments on both synthetic and experimental data using frequency-domain zero-padding method. Results show that the second NDoF is more accurate than traditional NDoF for high-contrast scatterers, and the third NDoF can be easily estimated by the traditional NDoF plus a positive constant, which provides a simple way to estimate the upper bound of NDoF.

A closed-form non-line-of-sight (NLOS) turbulenceinduced fluctuation model is derived for ultraviolet scattering communication (USC), which models the received irradiance fluctuation by Meijer G function. Based on this model, we investigate the error rates of the USC system in NLOS case using different modulation techniques. Closed-form error rate results are derived by integration of Meijer G function. Inspired by the decomposition of different turbulence parameters, we use a series expansion of hypergeometric function and obtain the error rate expressions by the sum of four infinite series. The numerical results show that our error rate results are accurate in NLOS case. We also study the relationship between the turbulence influence and NLOS transceiver configurations. The numerical results show that when two-LOS link formulates the same distance, the turbulence influence is the strongest for long ranges and the weakest for short ranges.

In this paper, we propose the neural Born iterative method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iterative method (TBIM). NeuralBIM employs independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions regarding the residuals. Two different schemes are presented in this paper, including the supervised and unsupervised learning schemes. With the data set generated by the method of moments (MoM), supervised NeuralBIM are trained with the knowledge of total fields and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded objective function founding on the governing equations of ISPs, which results in no requirement of total fields and contrasts for training. Numerical and experimental results further validate the efficacy of NeuralBIM., Comment: This preprint has been published in IEEE Transactions on Antennas and Propagation on 01 November 2022. Please cite the final published version as [T. Shan et al., "Neural Born Iterative Method for Solving Inverse Scattering Problems: 2D Cases," in IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 818-829, Jan. 2023, doi: 10.1109/TAP.2022.3217333.]

In this study, physics-informed supervised residual learning (PhiSRL) is proposed to enable an effective, robust, and general deep learning framework for 2D electromagnetic (EM) modeling. Based on the mathematical connection between the fixed-point iteration method and the residual neural network (ResNet), PhiSRL aims to solve a system of linear matrix equations. It applies convolutional neural networks (CNNs) to learn updates of the solution with respect to the residuals. Inspired by the stationary and non-stationary iterative scheme of the fixed-point iteration method, stationary and non-stationary iterative physics-informed ResNets (SiPhiResNet and NiPhiResNet) are designed to solve the volume integral equation (VIE) of EM scattering. The effectiveness and universality of PhiSRL are validated by solving VIE of lossless and lossy scatterers with the mean squared errors (MSEs) converging to $\sim 10^{-4}$ (SiPhiResNet) and $\sim 10^{-7}$ (NiPhiResNet). Numerical results further verify the generalization ability of PhiSRL., Comment: This preprint has been published in IEEE Transactions on Antennas and Propagation on 01 March 2023. Please cite the final published version as [T. Shan et al., "Physics-Informed Supervised Residual Learning for Electromagnetic Modeling," in IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3393-3407, April 2023, doi: 10.1109/TAP.2023.3245281]

Hand and arm gesture recognition using the radio frequency (RF) sensing modality proves valuable in manmachine interface and smart environment. In this paper, we use curve matching techniques for measuring the similarity of the maximum instantaneous Doppler frequencies corresponding to different arm gestures. In particular, we apply both Frechet and dynamic time warping (DTW) distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, take into account both the location and the order of the points for rendering two curves similar or dissimilar. It is shown that improved arm gesture classification can be achieved by using the DTW method, in lieu of L2 and L1 distances, under the nearest neighbor (NN) classifier., Comment: 6 pages, 7 figures, 2020 IEEE radar conference. arXiv admin note: substantial text overlap with arXiv:1910.11176

In considering human-machine interface (HMI) for smart environment, a simple but effective method is proposed for automatic arm motion recognition with a Doppler radar sensor. Arms, in lieu of hands, have stronger radar cross-section and can be recognized from relatively longer distances. An energy-based thresholding algorithm is applied to the spectrograms to extract the micro-Doppler (MD) signature envelopes. The positive and negative frequency envelopes are concatenated to form a feature vector. The nearest neighbor (NN) classifier with Manhattan distance (L1) is then used to recognize the arm motions. It is shown that this simple method yields classification accuracy above 97 percent for six classes of arm motions. Despite its simplicity, the proposed method is superior to those of handcrafted featurebased classifications and low-dimension representation techniques based on principal component analysis (PCA), and is comparable to convolutional neural network (CNN)., Comment: 9 pages, 11 figures

We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies which are generated during each gesture act. We detect the positive and negative envelopes separately, and form a feature vector of their augmentation. We use the $k$-nearest neighbor ($k$NN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates., Comment: 6 pages, 7 figures, 2019 radar conference

In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time-frequency (TF) representation framework, where a TF kernel is designed by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts due to missing observations while preserving the FH signal auto-terms. The kernelled results are represented in the instantaneous autocorrelation function domain, which are then processed using a re-designed structure-aware Bayesian compressive sensing algorithm to accurately estimate the FH signal TF spectrum. The proposed method achieves high-resolution FH signal spectrum estimation even when a large portion of data observations is missing. Simulation results verify the effectiveness of the proposed method and its superiority over existing techniques., Comment: 14 pages, 11 figures, to appear in IEEE Transactions on Signal Processing

In this work, we investigated the feasibility of applying deep learning techniques to solve Poisson's equation. A deep convolutional neural network is set up to predict the distribution of electric potential in 2D or 3D cases. With proper training data generated from a finite difference solver, the strong approximation capability of the deep convolutional neural network allows it to make correct prediction given information of the source and distribution of permittivity. With applications of L2 regularization, numerical experiments show that the predication error of 2D cases can reach below 1.5\% and the predication of 3D cases can reach below 3\%, with a significant reduction in CPU time compared with the traditional solver based on finite difference methods., Comment: 7 pages, 10 figures

The asymmetric electron dispersion in type-II Weyl semimetal theoretically hosts anisotropic transport properties. Here we observe the significant anisotropic Adler-Bell-Jackiw (ABJ) anomaly in the Fermi-level delicately adjusted WTe$_{1.98}$ crystals. Quantitatively, $C_w$ , a coefficient representing intensity of ABJ anomaly, along a- and b-axis of WTe$_{1.98}$ are 0.030 and 0.051 T$^{-2}$ at 2 K, respectively. We found that temperature-sensitive ABJ anomaly is attributed to topological phase transition from type-II Weyl semimetal to trivial semimetal, which is verified by first-principles calculation using experimentally determined lattice parameters at different temperatures. Theoretical electrical transport study reveals that observation of ansotropic ABJ both along a- and b-axis in WTe$_{1.98}$ is attributed to electrical transport in the quasi-classical regime. Our work may suggest that electron-doped WTe$_2$ is an ideal playground to explore the novel properties in type-II Weyl semimetals., Comment: 20 pages, 5 figures, 1 table

Recently, the extremely large magnetoresistance observed in transition metal telluride, like WTe$_2$, attracted much attention because of the potential applications in magnetic sensor. Here we report the observation of extremely large magnetoresistance as 3.0$\times$10$^4$ % measured at 2 K and 9 T magnetic field aligned along [001]-ZrSiS. The significant magnetoresistance change (~1.4$\times$10$^4$ %) can be obtained when the magnetic field is titled from [001] to [011]-ZrSiS. These abnormal magnetoresistance behaviors in ZrSiS can be understood by electron-hole compensation and the open orbital of Fermi surface. Because of these superior MR properties, ZrSiS may be used in the novel magnetic sensors., Comment: 18 pages, 5 figures, 21 references

Orthorhombic SrIrO3 is a typical spin-orbit-coupling correlated metal that shows diversified physical properties under the external stimuli. Here nonlinear Hall effect and weakly temperature-dependent resistance are observed in a SrIrO3 film epitaxially grown on SrTiO3 substrate. It infers that orthorhombic SrIrO3 is a semimetal oxide. However, linear Hall effect and insensitive-temperature-dependent resistance are observed in SrIrO3 films grown on (La,Sr)(Al,Ta)O3 (LSAT) substrates, suggesting a tunable semimetallic state due to band structure change in SrIrO3 films under different compressive strain. The mechanism of this evolution is explored in detail through strain-state analysis by reciprocal space mapping and electron diffraction, carrier density and mobility calculations, as well as electronic band structure evolution under compressive strain (predicted by tight-binding approximation). It might suggest that the strain-induced band shift leads to the semimetallic tuning in the SrIrO3 film grown on from SrTiO3 to LSAT substrates. Our findings illustrate the tunability of SrIrO3 properties and pave the way to induce novel physical states in SrIrO3 such as the proposed topological insulator state in heterostructures., Comment: 21 pages, 5 figures, submitted to PRB

Spin orbit coupling plays a non-perturbation effect in many recently developed novel fields including topological insulators and spin-orbit assistant Mott insulators. In this paper, strongly temperature-dependent spin orbit coupling, revealed by weak anti-localization, is observed at low temperature in 5d strongly correlated compound, SrIrO3. As the temperature rising, increase rate of Rashba coefficient is nearly 30%-45%/K. The increase is nearly 100 times over that observed in semiconductor heterostructures. Microscopically, the large increase of Rashba coefficient is attributed to the significant evolution of effective Land\'e g factor on temperature, whose mechanism is discussed. Sensitively temperature-dependent spin orbit coupling in SrIrO3 might be applied in spintronic devices, Comment: 8 pages 5 figures