Channel estimation in reconfigurable intelligent surface (RIS) assisted communications requires high pilot overhead due to numerous RIS elements incapable of signal processing. Recently, research on sensing RIS has provided a dimension-independent channel estimation scheme with merely three pilots. Nevertheless, it assumes that the BS-RIS channel is perfectly known to the RIS and remains invariant over a prolonged period, while inducing high hardware and power consumption. To address these issues, this paper introduces a generalized approximate message passing (GAMP) based channel estimation framework to achieve dimension-independent estimation of separate channels, without the assumption of known BS-RIS channel. Specifically, we first formulate the channel estimation problem in RIS assisted communications as a compressive phase retrieval problem. Based on the phaseless power observations, we leverage the GAMP algorithm to retrieve original sparse signals, which inherently supports the sparse-sampling sensing RIS architecture. Furthermore, by exploiting the intrinsic mapping between the sparse representations of the channels and the power observations in the angular domain, we propose a learned GAMP network to enhance the convergence stability and estimation accuracy. Finally, simulation results demonstrate that the proposed approach can efficiently estimate both the BS-RIS and UE-RIS channels with four pilots, while eliminating the requirement for prior knowledge of the BS-RIS channel and significantly reducing hardware and power consumption.

In this study, we investigate the application of the stochastic path integral (SPI) for 2D magnetotelluric (MT) modeling. Our goal is to assess whether electromagnetic numerical algorithms can mechanistically adapt to parallel heterogeneous computing architectures and maximize the conversion of computational power into efficient electromagnetic field simulations. Using the Feynman-Kac formula, we derive a path integral representation of the MT Helmholtz equation in a stochastic framework. We then investigate the online-offline two-stage MT-SPI algorithm that includes Monte Carlo random walks, mapping matrix construction, and large-scale matrix-vector multiplication. Numerical experiments verify the correctness and stability of the proposed SPI algorithm. At the relative error of 1.2%, SPI simultaneously achieves over 550× acceleration and approximately 30% memory reduction on GPU under a MATLAB-based implementation, compared to finite difference method (FDM).

The Pulmonary Function Test (PFT) is a widely utilized and rigorous classification test for evaluating lung function, serving as a comprehensive diagnostic tool for lung conditions. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder (VAE) with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then stacked to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data from 137 subjects were utilized for the training phase. Initially, the model underwent validation through a leave-one-out cross-validation process. During this validation, the model achieved an accuracy and sensitivity of 0.96 and 1.00, respectively, with an f1-score of 0.98 when identifying the normal subjects. To assess pipeline reliability and feasibility, we tested it on 9 newly recruited subjects, with accurate ventilation mode predictions for 8 out of 9. In addition, we included 2D EIT results for comparison and conducted ablation experiments to validate the effectiveness of the VAE. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.

Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the feature space are optimized by minimizing the misfit between measured and simulated data. A dataset of 3D heads characterized by permittivity and conductivity is constructed to train the VAE. Numerical examples show that our method increases structural similarity by 14% and speeds up the solution process by over 3 orders of magnitude using only 4.8% number of the unknowns compared to the voxel-based method. This high-resolution imaging of electrical properties leads to more accurate stroke diagnosis and offers new insights into the study of the human brain.

Reconfigurable intelligent surface (RIS) has been widely considered as a key technique to improve spectral efficiency for the 5th generation (5G) and beyond 5G (B5G) communications. Compared with most existing research that only focuses on the reflective RIS, the design and prototyping of a novel transmissive RIS are presented in this paper, and its enhancement to the RIS-aided communication system is experimentally demonstrated. The 2-bit transmissive RIS element utilizes the penetration structure, which combines a 1-bit current reversible dipole and a 90° digital phase shifter based on a quadrature hybrid coupler. A transmissive RIS prototype with$16\times16$elements is designed, fabricated, and measured to verify the proposed design. The measured phase shift and insertion loss of the RIS element validate the 2-bit phase modulation capability. Being illuminated by a horn feed, the prototype achieves a maximum broadside gain of 22.0 dBi at 27 GHz, and the two-dimensional beamforming capability with scan angles up to ±60° is validated. The experimental results of the RIS-aided communication system verify that by introducing the extra gain and beam steering capability, the transmissive RIS is able to achieve a higher data rate, reduce the transmit power, improve the transmission capability through obstacles, and dynamically adapt to the signal propagation direction.

Magnetotelluric (MT) data inversion reconstructs an electrical resistivity structure most compatible with the observed MT data, and static correction can remove the undesired static shift effect in MT data. Conventional MT data static shift correction often faces the challenge of demanding requirements, such as large data amount, additional types of data, or a deep understanding of the research area. MT inversion constrained by seismic data often has better resolution and model consistency compared with independent MT inversion. However, valuable inversion knowledge contained in geophysicists’ expertise is not effectively incorporated. In this work, we present an intelligent MT data inversion method leveraging data- and physics-driven techniques based on deep learning. A novel MT data static shift correction method is introduced based on a neural network (NN). An MT data inversion method is formulated with the constraint of the extracted seismic reflection image based on two different NNs. Experiments on synthetic and field data verify the effectiveness of the proposed method.

In this article, multitask learning is applied to forward modeling of 2-D magnetotellurics (MT) to predict the apparent resistivity and impedance phase of MT data. Multitask learning can learn multiple objectives simultaneously based on the shared representation, thereby improving efficiency and accuracy. The loss function is carefully designed by weighing multiple objective functions based on homoscedastic uncertainty, and the structural similarity regularization term is applied to ensure the texture of the obtained apparent resistivity and impedance phase. The proposed convolutional neural network can make accurate predictions with an average relative error of apparent resistivity and impedance phase less than 1.2% and 0.2%, respectively. The generalization ability of the proposed network is verified by applying it to cases with more complex resistivity distributions than training samples. This article shows the potential for fast and accurate computation of two highly correlated physical quantities in electromagnetic fields.