Search

In this letter, a new deep learning (DL) solver is introduced, offering a rapid and effective full-wave computational method for simulating the electromagnetic forward (EMF) process. The core of this solver is the deep residual convolutional neural network (DRCNN) architecture. The DRCNN ingests the incident EM field along with the permittivity contrast distribution within the specified region of interest (ROI), and in turn, it generates the resultant EM field that is influenced by the incident EM wave. During its offline training, an EM scattering simulation tool has been developed to calculate the EM scattered fields. The loss function includes two distinct components: the data-induced loss, which quantifies the discrepancy between the DRCNN's predicted total EM fields and the actual labeled EM fields and the physics-induced loss, which measures the variance between the EM scattered fields derived from the actual labeled total EM fields and the scattered fields generated from the DRCNN's predictions. The training of this proposed EMF solver only relies on a simple synthetic dataset. Compared with traditional approaches, the proposed DL solver addresses EMF issues with notable precision while significantly cutting down on computational duration. Numerical benchmarks have demonstrated the viability of this DL solver.

This communication proposes a novel electromagnetic (EM) inversion solver based on a two-step deep learning (DL) framework. The framework consists of the deep convolutional asymmetric encoder–decoder structure (DCAEDS) followed by the deep residual convolutional neural network (DRCNN). In the first step, DCAEDS utilizes EM scattered field data from a single-frequency one-time measurement to coarsely retrieve the initial contrasts (permittivities) of target scatterers. In the second step, DRCNN employs a mixed input scheme, comprising the initially reconstructed permittivities from the first step and the original EM scattered field data, to significantly improve the retrieved contrasts (permittivities) and refine the reconstruction of targets. Consequently, the proposed EM inversion solver achieves excellent accuracy and efficiency, even for high-contrast targets. The proposed solver is flexible as it is required only for a single-frequency one-time measurement on the EM scattered field. Moreover, the proposed two-step DL-based solver overcomes the limitations of conventional methods, such as high computational costs and ill-posedness. Numerical benchmarks based on various dielectric objects demonstrate the feasibility of the proposed EM inversion solver, highlighting its potential as a candidate for real-time quantitative EM inversion for high-contrast targets.

In this communication, we propose to use a deep learning (DL) approach to detect unit failure in array antennas. Due to natural machine life cycle and/or unexpected accidents, antenna units unavoidably suffer from the risk of failure, leading to the deterioration of array performance. To realize the detection of unit failure, the far-field radiation patterns are used as the input of the deep convolutional neural network (DConvNet) for antenna array diagnosis learning. The proposed DConvNet consists of continuous functional groups of convolution, batch normalization, and activation layers, followed by a fully connected layer to realize recognition, i.e., the fault diagnosis of antenna array. Different from conventional diagnosis techniques, the main advantage of the proposed DL approach does not require intensive computations based on Green’s function. The training data are collected by the electromagnetic (EM) simulation tool. Additionally, the Gaussian noise is added to the training data to imitate the interference in real application scenarios. The proposed DConvNet for array diagnosis is verified by three numerical benchmarks and demonstrates that it can diagnose antenna array in a complex environment with generality.

This communication proposes a novel deep-learning (DL) framework for the electromagnetic inverse scattering (EMIS) problems. Solving EMIS problems is a challenging topic due to various difficulties, such as intrinsic nonlinearity, high computation cost, high contrast, and so on. To overcome these challenges, a novel DL-inspired approach is presented in the context of conditional deep convolutional generative adversarial network (CDCGAN), termed CDCGAN-EMIS. The proposed CDCGAN is based on a generator with an EM forward solver and the corresponding discriminator, both constructed by deep convolutional neural networks (DConvNets). During the offline training step, the generator learns a distribution between the measured scattered field data and the corresponding contrasts (permittivities) of dielectric scatterers, while the discriminator determines whether the presented samples are real or fake. Therefore, such CDCGAN-EMIS can generate contrasts of scatterers from measured scattered field data, by learning the distribution between the known contrasts of scatterers and their corresponding field and generating solutions. Based on the proposed CDCGAN-EMIS, EMIS problems can be accurately solved even for extremely high-contrast scatterers. Numerical examples indicate the accuracy and feasibility of our method. The proposed CDCGAN-EMIS opens a novel path for the DL-inspired real-time quantitative microwave imaging method for high-contrast scatterers.

This article proposes a novel fast electromagnetic (EM) inversion solver for heterogeneous scatterers with high contrast. In order to ensure inversion accuracy, the conventional solvers for the EM inverse scattering (EMIS) problems usually require EM scattered field information originating from multiple incident EM waves, resulting in tedious measurement operation and considerable measurement data for inversion computation. Thus, the conventional solvers are difficult to suit for real-time quantitative EM problems, which require relatively simple measurement operations and small measurement data dimensions. To overcome these challenges, a novel EM inversion solver is proposed based on a conditional deep convolutional generative adversarial network (CDCGAN). The proposed CDCGAN includes the generator with an EM scattering simulator and the corresponding discriminator, both consisting of complex-valued deep convolutional neural networks (DConvNets). The trained CDCGAN can be successfully applied to inhomogeneous and high-contrast scatterers only by employing one single incident EM wave in single-frequency and far-field measurement, which greatly simplifies measurement and reduces the computation complexity for its further inversion computation. Numerical examples have indicated the accuracy and feasibility of the proposed DL-based inversion solver, which acts as the potential candidate for solving real-time quantitative EM problems.

This letter proposes a novel deep learning (DL) based fast solver for the electromagnetic forward (EMF) process. This proposed fast full-wave solver for EMF process is designed based on the deep conditional convolutional autoencoder (DCCAE), consisting of a complex-valued deep convolutional encoder network and its corresponding complex-valued deep convolutional decoder network. The encoder network makes use of the input consisting of the incident electromagnetic (EM) wave and the contrast (permittivities) distribution of the target domain, while the corresponding decoder network predicts the total EM field illuminated by the input incident EM wave. The training of the proposed DCCAE solver for EMF is merely based on the simple synthetic dataset. Thanks to its strong approximation capability, the proposed DCCAE can realize the prediction of the EM field of target domain by using the incident EM field and the distribution of contrasts (permittivities). Therefore, compared with conventional methods, the EMF problem could be solved with higher accuracy and the significant reduced computation time. Numerical examples have illustrated the feasibility of the newly proposed DL-based EMF solver. The newly proposed DL-based EMF solver presents its excellent performance for the real-time online application.

To meet the requirement of capturing tobacco-specific nitrosamines (TSNA) for environment protection, a unique microenvironment was carefully created inside the channels of mesoporous silica MCM-41. In situ carbonization of template micelles at 923 K, combined with the excess aluminum used in one-pot synthesis of MCM-41, is adopted to tailor the tortuosity of mecsoporous channels, while loaded metal oxides (5 wt %) and the Al component in the framework are employed to exert the necessary electrostatic interaction toward the target carcinogens TSNA in solution. The elaborated microenvironment created in mesoporous sorbents was characterized with XRD, N2adsorption–desorption, TEM, XPS, and TG-DSC methods. Various solutions of Burley- and Virginia-type tobaccos were used to assess the adsorption performance of new mesoporous sorbents, and the influence of the solid-to-liquid ratio, adsorption time, and loading amount of CuO on the adsorption was carefully examined. The representative sample 5%Cu/AM-10c could capture 27.2% of TSNA in Burley tobacco solution, and its capacity reached 0.3 mg g–1in Snus tobacco extract solution, offering a promising candidate for the protection of the environment and public health.