A new broadband DC-bias included model for radio frequency (RF) power transistors is presented in this paper. The proposed method utilizes the canonical section-wise piecewise linear (CSWPL) model framework in order to incorporate both frequency as well as bias information simultaneously. Detailed descriptions of the fundamental theory of the developed model are provided, along with their validation through experiments. The model is implemented in commercial software and validated using DC and RF tests with measured load-pull data from a 10-W GaN device. In contrast to standard and bias-included CSWPL models, this model can predict transistor behavior under a variety of bias voltages and frequencies using only one set of parameters, thereby significantly reducing model complexity. Additionally, the proposed model is applied to a multi-octave PA design to provide further validation. Measurements made on the realized PA are compared to simulations based on the proposed model. Validity of the extracted model is confirmed by the agreement between measurements and simulations.

This paper introduces a new method for designing power amplifiers (PAs) using a flexible, automatic approach. It employs a Gaussian process-assisted fast non-dominated sorting genetic algorithm (GP-NSGA-II) for both circuit synthesis and layout optimization. Traditional genetic algorithms are computationally intensive and have poor convergence, making them difficult to use with three-dimensional full-wave simulators. To address these issues, the authors incorporate Gaussian process regression from Bayesian optimization into NSGA-II, improving convergence and computational efficiency. The method integrates layout simulation and optimization, enabling automatic PA design with direct layout optimization. To demonstrate the method’s effectiveness, the authors design a wide-band PA and a tri-band PA using a 10-W gallium-nitride (GaN) high electron mobility transistor (HEMT). The tests show that the prototypes perform well: the wide-band PA has a power-added efficiency (PAE) of over 61% and an output power greater than 41.5 dBm in the 2-3 GHz band, while the tri-band PA achieves PAE values of over 59%, 55%, and 57% in the 0.7-1.1 GHz, 2.3-2.5 GHz, and 3.4-3.5 GHz bands, respectively, with output powers exceeding 41.2 dBm, 40.6 dBm, and 40.6 dBm.

Artificial intelligence (AI) techniques are increasingly being used for the inverse design of microwave devices. However, several challenges, including intensive computation costs for training samples, high-dimensional data, nonuniformity, and low-quality samples in the design space, can negatively impact the final modeling performance. To alleviate these issues, a high-quality sampling inverse design scheme incorporating wavelet transform deep learning (HQS-WTDL) is proposed to achieve customized, automated microwave filter design. In the forward simulation-based sampling, particle swarm optimization (PSO) is used to tentatively select rule-defined samples. Multilabel synthetic minority over-sampling technique (MLSMOTE) is then applied to enlarge the training samples and improve their uniformity in the design space. An inverse modeling approach using neural networks to map the nonlinear relationship between a given set of S-parameters and required filter structural parameters is presented. Given that the dimension of the S-parameters is much higher than that of the structure parameters, the corresponding neural network used in this approach is deep and complex, with multiple layers and neurons. To reduce the number of input variables, the S-parameters are subjected to wavelet transformation, allowing for more efficient representation by the neural network. The proposed method is validated using a band-pass microstrip hairpin filter as an example. Results demonstrate that the proposed approach achieves better modeling effectiveness and inverse design efficiency than conventional methods. In addition, the proposed method allows for fast customization of device parameters, such as center frequencies and bandwidths with good prediction accuracy.

Electromagnetic computational imaging offers a promising solution to electromagnetic inverse scattering problems. Whereas, it is challenged by its ill-posed nature and non-linearity. Traditional iterative methods are often slow and prone to local minima, while recent deep generative models overlook the physical principles that govern the transformation from scattering fields to images of constitute parameters, limiting their interpretability, generalization, and robustness. To address these issues, we propose ScatDiff, a novel Scatter-to-image Diffusion model that integrates electromagnetic data with fundamental physical principles. ScatDiff uses a time-aware, backpropagation-enhanced diffusion to generate noisy images embedded with electromagnetic priors, along with a denoising module that uses cross-attention to adaptively integrate scattering fields. Additionally, a physics-driven reconstruction module incorporates an induced current model into the loss function to enhance interpretability. Experiments on three MNIST variants, Gesture dataset, the “Austria” profile, and the “FoamDielExt” profile show that ScatDiff outperforms traditional iterative methods and deep learning models in both imaging quality and efficiency, with strong generalization and robustness under high noise conditions. Code and datasets are available on https://github.com/Scatdif.

Object imaging and recognition under difficult visual conditions is extremely challenging due to the captured low-quality images, and traditional optical-based recognition methods always fail in this task. In this paper, we propose to utilize the visual-microwave image pairs captured by both visual cameras and microwave sensors for imaging and recognition. To address the heavy noises in the low-quality optical images, we retrieve the physically quantitative images from associated scattered field data, and enhance visual features by both optical and retrieval images. We develop a cross-modal Enhanced Attentive Visual-Microwave Fusion (EAVMF) object recognition model to jointly learn the cross-modal generator and multimodal recognizer. In addition, an attention module for the visual subnetwork is utilized to highlight the regions of interest. Two multimodal datasets with synthetic visual-microwave image pairs are built to simulate the difficult visual condition. The numerical results on these datasets demonstrate that: 1) both the multimodal fusion, cross-modal enhancement, and visual attention module can enhance the performance; and 2) compared with existing methods, the proposed EAVMF not only performs better in terms of accuracy but also has good scalability and one-shot learning ability.

Inspired by animals’ long-distance migration behaviors, we proposed a novel and reliable long-distance true navigation method based on the measurement of geomagnetic fields. By establishing a two-dimensional (2D) gradient approaching coordinate plane with geomagnetic intensity and inclination, long-distance true navigation can be achieved from any starting spots in this area. Without other calibration and auxiliary information, this navigation method is highly independent, which can be used in electromagnetic shielding circumstances, such as deep ocean. The geomagnetic intensity and inclination gradients are estimated with real-time measurement of the geomagnetic field along the navigation trajectory. With estimated local gradients, 2D gradient approaching algorithm can be applied to predict the heading direction, and the agent can press on towards the destination step by step. Theoretical analysis and Monte Carlo simulations verified the feasibility and practicality of this method, proving its robustness in the presence of interference and different measurements errors. It can be a promising candidate in the design of navigation systems used in autonomous underwater vehicles or platform.

In the petroleum industry, downhole water-cut loggings are essential for reservoir management. Previously, most of them are based on permittivity measurements by low-frequency capacitance methods. However, they fail to work in aging oil fields with high water cuts and highly mineralized subterranean water. In this article, a nonresonant self-calibrated wideband microwave sensor is proposed to solve this problem. Taking advantage of the Cerenkov-like enhanced radiation of coaxial leaky-wave antennas, sufficient signals can be received even when the fluid under test (FUT) is highly conductive. An accurate wideband forward model has been established with no empirical or undetermined quantities. Complex permittivities and water fractions of the FUT can be retrieved without calibrations, making the sensor especially suitable for the downhole working conditions with unknown temperature drifts. NaCl solutions with different concentrations (0–130 g/L), oil–water emulsions with different water fractions (70%–100%), and ethanol–water solutions have been tested in the frequency band ranging from 1 to 8 GHz. Dielectric constants and conductivities of the NaCl solutions range from 40 to 80 and from 0 to 15 S/m, respectively, covering the main measurement ranges of high-water-cut monitoring.

The end-to-end scalable cascaded convolutional neural networks (SC-CNNs) are proposed to solve inverse scattering problems (ISPs), and the high-resolution image can be directly obtained from the scattered field with the guiding by multiresolution labels in the cascaded blocks. To alleviate the difficulty of solving the ISPs via a full-wave way, the proposed SC-CNNs are physically decomposed into two parts, i.e., the linear transformation and the multiresolution imaging networks. The first part is composed of one CNN block and is used to mimic the linear transformation [e.g., backpropagation (BP)] from scattered field to the preliminary image, whereas the second part consists of a few cascaded CNN blocks to realize the reconstruction from the rough image to high-resolution image. With more high-frequency components incorporating into the multiresolution labels, the cascaded networks can be guided through those labels, avoiding black-box operations and enhancing the physical meaning and interpretability. The proposed SC-CNNs are verified by both the synthetic and experimental examples and it is proved that better performance can be achieved in terms of both inversion accuracy and efficiency compared to the BP-Unet and direct inversion scheme (DIS).

A multiplicatively regularized iterative updated background method (MR-IUBM) based on difference integral equations is proposed to solve the electromagnetic inverse scattering problems (ISPs) involving both inhomogeneous and homogeneous background media. This aims to improve the ability for solving highly nonlinear ISPs with source-typed inversion solver by consistently updating the inhomogeneous background media from the updated unknown scatterers via the use of the Green's function with homogeneous medium. In virtue of MR into the cost function, the stability and the capability of tackling highly nonlinear ISPs can be further greatly improved. Synthetic and experimental results are presented to illustrate the efficiency of this method.

Induction logging through casing (ILTC) is of significant importance to the evaluation of residual oils. Traditional induction logging fails to work in cased wells due to the shielding effect of metallic casings. To date, the feasibility of ILTC remains an open question. In this article, we propose an ILTC based on the detection of lateral waves that carry the formation information. Based on the analytical equations derived to describe the hybrid modes of total magnetic fields that can be detected inside cased wells, numerical analyses aiming to describe the behavior of lateral waves were carried out in both frequency and time domains. The obtained results comply with full-wave simulations and existing scaled experiments. Our results indicate that if an ILTC tool can be properly configured to work in a region dominated by lateral waves, and the casing effect and the primary field of the lateral waves can be effectively compensated, it is feasible to implement an ILTC to detect formation conductivities ranging from 0.01 to 5 S/m.

Industrial gas leaks cause accidents and pose threats to the environment and human life. Thus, it is essential to detect gas leaks in time. Usually, the abnormal concentration signals are defined by a fixed concentration value, such as 25% of the lower explosive limit. However, it is difficult to accumulate to the fixed point quickly when the leak is small. In addition, the actual leak signals are seldom available, making many data classifications inoperable. To solve these problems, this paper proposes a detection approach using the auto-correlation function (ACF) of the normal concentration segment. The feature of each normal segment is obtained by calculating the correlation coefficients between ACFs. According to the features of statistical analysis, a nonconcentration threshold is determined to detect the real-time signals. In addition, the weighted fusion algorithm based on the distance between the sensors and virtual leak source is used to fuse multisensory data. The proposed method has been implemented in a field by building a wireless sensor network. It is confirmed that the system detection rate reaches as high as 96.7% and the average detection time delay is less than 30 s on the premise of low false alarm rate.

This paper presents a novel compact Ku-band active electronically steerable antenna array design with a low-cost and integrated T/R 3D module employed for airborne synthetic aperture radar (SAR) systems. The entire system adopts 3D multilayer technology with vertical interconnection to construct the hermetically packaging RF modules. By assembling different multifunctional modules into a whole multilayer board, the 3D T/R technique greatly improves the system integration and reduces implementation cost and size. Besides, a wideband circular polarized antenna array was designed in LTCC and connected to the proposed T/R modules to form a complete AESA. The whole proposed antenna system has been fabricated and experimentally investigated. Measurement results showed very good phased array performances in terms of gain, axial ratio, and radiating patterns. The low-cost, lightweight, and low-power features exhibited by the proposed design validate its applicability for weight and power constrained platforms with great electronic steering ability.

This paper proposes a new broadband Doherty power amplifier topology with extended back‐off range. A shunted λ /4 short line or λ /2 open line working as compensating reactance is introduced to the conventional load modulation network, which greatly improves its bandwidth. Underlying bandwidth extension mechanism of the proposed configuration is comprehensively analyzed. A three‐device Doherty power amplifier is implemented for demonstration based on Cree’s 10 W HEMTs. Measurements show that at least 41% drain efficiency is maintained from 2.0 GHz to 2.6 GHz at 8 dB back‐off range. In the same operating band, saturation power is larger than 43.6 dBm and drain efficiency is higher than 53%.

In this paper, we combine two techniques together, i.e., the fast Fourier transform-twofold subspace-based optimization method (FFT-TSOM) and multiplicative regularization (MR) to solve inverse scattering problems. When applying MR to the objective function in the FFT-TSOM, the new method is referred to as MR-FFT-TSOM. In MR-FFT-TSOM, a new stable and effective strategy of regularization has been proposed. MR-FFT-TSOM inherits not only the advantages of the FFT-TSOM, i.e., lower computational complexity than the TSOM, better stability of the inversion procedure, and better robustness against noise compared with the SOM, but also the edge-preserving ability from the MR. In addition, a more relaxed condition of choosing the number of current bases being used in the optimization can be obtained compared with the FFT-TSOM. Particularly, MR-FFT-TSOM has even better robustness against noise compared with the FFT-TSOM and multiplicative regularized contrast source inversion (MR-CSI). Numerical simulations including both inversion of synthetic data and experimental data from the Fresnel data set validate the efficacy of the proposed algorithm.