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Using artificial neural networks (NNs) in microwave medical diagnosis is recently of great interest in various problems such as early breast cancer detection, brain stroke, and leukemia monitoring. NNs facilitate the process by directly assessing the presence and properties of the tissues based on the scattered field values. Although the reported studies obtained successful results through the application of NNs to microwave diagnostic problems, they used large numbers of input data. The NN input, referred to as features, for microwave diagnosis is composed of scattered fields namely antenna transmission and reflections at the frequency of choice. Large input data increase both the number of required training samples and computational cost. Optimizing the number of antennas and frequency of operation is therefore critical to improving the performance of NN-based medical diagnosis. This work considers the correlations between the effects of different frequencies and receiver/transmitter (Rx/Tx) antennas separately in order to objectively reduce the number of features. Optimized feed-forward NNs are applied to detect the presence of object(s) with permittivity value above the predefined level within the solution domain. It is performed by designating various permittivity values to the internal object(s). Promising results were obtained by reducing the number of features approximately seven times. [ABSTRACT FROM AUTHOR]

Ultrawideband (UWB) microwave detection technology, which is low cost and harmless, has been intensively studied and developed for breast cancer detection. In this study, a composite end-to-end framework that consists of convolutional neural network (CNN) and long-short-term memory (LSTM) is proposed, which can realize the tasks of detecting and quadrant locating the breast tumor simultaneously without any complicated microwave imaging processing. In order to verify the proposed network, three datasets are constructed. First, the microwave signals are solved by the auxiliary differential equation-finite-difference time-domain (ADE-FDTD) solver and the magnetic resonance imaging (MRI)-derived breast models with varied breast densities. Then, Simulation datasets 1 and 2 with varied dielectric properties are constructed. Furthermore, an experimental dataset is constructed through an experimental system consisting of the UWB pulse generation module, UWB antennas, the realistic breast phantom, and sampling oscilloscope. With the proposed network, the overall prediction accuracies of the three datasets reach 99.56%, 98.94%, and 89.50%. These promising results demonstrate the effectiveness and accuracy of the proposed deep learning framework for microwave breast cancer detection. [ABSTRACT FROM AUTHOR]

This article presents a novel and efficient batch data selection method based on active and unsupervised learning in real-time near-field scanning. The new approach shows a remarkable advantage over random sampling in reducing the number of scanning data samples and the total scanning time. Moreover, careful hyperparameter tuning is unnecessary, as the performance is insensitive to the hyperparameter values. After randomly selecting some initial points, three key steps are sequentially and iteratively implemented. First, the query-by-committee (QBC) method is adopted to evaluate the uncertainties of the unobserved positions using different interpolation functions and select an “uncertain” group with the largest variances. Second, the weighted K-means clustering (WKMC) method divides the “uncertain” group into multiple clusters and selects the representative samples by choosing the most uncertain point in each cluster to ensure diversity. Finally, an estimated variance change (EVC) method is proposed to select the most representative samples to enhance diversity further. The proposed approach of this article, referred to as the QWE (QBC + WKMC + EVC) method, can well balance uncertainty and diversity with high efficiency. The newly proposed QWE approach is validated in both simulations and measurements of near-field scanning. The batch data selection method of this article can be extended to other multidimensional regression modeling problems in which data acquisition is expensive. [ABSTRACT FROM AUTHOR]

Microwave imaging holds the potential to bring an alternative to ionizing and expensive imaging modalities, such as mammography, magnetic resonance imaging (MRI), and computerized tomography (CT) scan. The absence of a high-resolution and non-model-based image reconstruction technique is one of the reasons that limited microwaves-based imaging. We introduce a microwaves-based imaging modality relying on the Radon transform technique adopted to create an imaging system identical in concept to CT-scanning methods that employ X-rays. This system uses low-frequency microwaves and highly localized radiators and receivers. While low-frequency microwaves are not perceived to have the ray-like behavior of X-rays, our system hinges on the principle that if the object under test (OUT) is electrically small (smaller than the wavelength), then the impression/projection due to an electrically very small localized source will be highly similar to a projection generated by a ray-like source. This phenomenon is due to the fact that a receiver closest to the highly localized transmitter will capture the highest energy. Several simulation studies and measurements demonstrate the ability of this technique to provide cross-sectional images of a 3-D object with high resolution. [ABSTRACT FROM AUTHOR]

A preliminary system for microwave thorax imaging is designed and experiments are conducted. A compact low-profile symmetrical dipole antenna operating at 915 MHz is designed for the system. An acrylic rectangular box is fabricated as the experiment phantom to mimic an ordinary human thorax. Graduated cylinders with different diameters are used as imaging targets. During measurement, each antenna acts in turn as a transmitter and other antennas act as the receivers. One-step supervised descent method (SDM) is adopted as the imaging algorithm. Results under different scenarios indicate that structural information can be reconstructed with the one-step SDM. This study verifies the feasibility of microwave thorax imaging. [ABSTRACT FROM AUTHOR]

A deep learning (DL) method is applied to modeling electromagnetic (EM) scattering for microwave breast imaging (MBI). The neural network (NN) accepts 2-D dielectric breast maps at 3 GHz and produces scattered-field data on an antenna array composed of 24 transmitters and 24 receivers. The NN was trained by 18000 synthetic digital breast phantoms generated by generative adversarial network (GAN), and the scattered-field data pre-calculated by method of moments (MOM). Validation was performed by comparing the 2000 NN-produced datasets isolated from the training data with the data computed by MOM. Finally, data generated by NN and MOM were used for image reconstruction. The reconstruction demonstrated that errors caused by NN would not significantly affect the image result. But, the computational speed of NN was nearly 104 times faster than the MOM, indicating that DL has the potential to be considered as a fast tool for EM scattering computation. [ABSTRACT FROM AUTHOR]

Due to its high resolution and deep penetration depth, microwave ultrawideband radar is a promising tool for the non-destructive testing (NDT) of transportation infrastructure. Microwave radiation can also be used to reconstruct the dielectric properties of objects and therefore can be used to detect an air cavity or metallic rust in concrete. We used contrast source inversion (CSI) as one of the most promising inverse scattering schemes. To resolve the observation domain limitations of NDT, radar imaging method, also known as range points migration (RPM) method, was first incorporated into the inverse scattering algorithm based on CSI as a region of interest (ROI) estimator, which substantially improves the accuracy of complex permittivity reconstruction. In addition, the ROI optimizing scheme based on the CSI cost function is used to enhance ROI accuracy. The effectiveness of the proposed methods is validated via finite-difference time-domain (FDTD)-based numerical simulation, which assumes typical NDT model. [ABSTRACT FROM AUTHOR]

Recently, many successes have been witnessed in the field of inverse scattering problems (ISPs) with deep learning schemes (DLSs). However, most of the studies focus on the spatial characteristics of interested targets, and little attentions are paid to the information from multiple incidences that can be treated as temporal information measured in a time series. In this work, a multiple-incidence framework (MIF) is introduced by incorporating the information from multiple incidences into an extra dimension of data space, helping to enrich physical knowledge in DLSs. A 3-D convolutional neural network (CNN) structure is further introduced to explore cross-frame information of the data space, which enables simultaneous utilizations of multiple-incidence information. The proposed MIF can be easily adapted into previous DLSs in the literatures, where two widely used DLSs including back-propagation scheme (BPS) and dominant current scheme (DCS) are used to demonstrate the advantages of the proposed framework. It is shown by extensive numerical and experimental examples that MIF enables consistent improvements for BPS and DCS. It is expected that the proposed MIF will also find its applications in real-time dynamic microwave imaging and multiple-frequency imaging by effectively using physical knowledge from temporal and frequency measurements, respectively. [ABSTRACT FROM AUTHOR]

In this article, a multilayer reflective polarization conversion metasurface with both high-frequency selectivity and low profile is proposed. The frequency selective polarization conversion metasurface (FSPCM) is composed of four metal layers separated by three substrates. The cross-shaped patch on the top layer is connected to the folded microstrip line on the bottom layer with two via holes through the circle slots inside two ground layers, which converts the linearly polarized electromagnetic incident wave into cross-polarized wave after reflection. The second-order response can extend to the third-order response by introducing a coupled half-wavelength microstrip resonator on the bottom metal layer, which achieves high-frequency selectivity with a transmission zero and high polarization conversion rate (PCR) without increasing the thickness. The equivalent circuit analysis, structure simulation, and experimental demonstration are conducted in detail; the frequency selectivity of the fabricated FSPCM at the lower and upper band edge is 94 and 192 dB/GHz, respectively; PCR is more than 97% from 4.85 to 5.12 GHz, and the thickness is only $0.028\lambda _{0}$ , where $\lambda _{0}$ is the free space wavelength at the center operating frequency. Our findings pave a new way for enhancing the anti-interference capability and electromagnetic compatibility in applications of communication system, microwave imaging, and stealth technology. [ABSTRACT FROM AUTHOR]

Millimeter-wave (MMW) imaging has been widely studied for applications, such as personnel screening and industrial noninvasive testing; however, real-time MMW imaging with a subcentimeter resolution and a large field of view (FoV) for a fast-moving target has been rarely reported to date. In this article, we present the design of such an imaging system at the $Ka$ -band utilizing the concept of multistatic sparse array. Microwave monolithic integrated circuits based on an InP-heterojunction bipolar transistor (HBT) technology are used for the transmitter and receiver chipsets. With the help of an improved backprojection algorithm and a computation platform based on field-programmable gate arrays, real-time imaging for a target moving as fast as about 10 m/s is demonstrated at a frame rate of over 20 frames/s. The imaging resolution is measured to be better than 6 mm and the FoV can reach over $1\times 1\,\,\text{m}^{2}$. Imaging of a moving person with concealed items is carried out to illustrate the system’s capability for personnel screening. To the best of the authors’ knowledge, this is the first real-time MMW sparse array imaging system built upon InP-HBT chipsets. [ABSTRACT FROM AUTHOR]

We present a simple derivation of the increment of the scattering and impedance parameters among antennas caused by the scattering from a target in an inhomogeneous nonmagnetic reciprocal medium. Although these formulas have been published in the literature, we provide a straightforward and elegant derivation using the electromagnetic (EM) reciprocity theorem and the circuit theory. In this way, we provide a deeper physical insight into the formulas crucial for most microwave imaging (MWI) algorithms. [ABSTRACT FROM AUTHOR]

Microwave tomography (MWT)-based control is a novel idea in industrial heating systems that demands fast data acquisition (DAQ) and real-time imaging algorithms. Uniform diffraction tomography (UDT) is one such technique that can provide real-time imaging. However, its single-input single-output data-based inverse scattering formulation can lead to time-consuming DAQ. In this article, a multistatic uniform diffraction tomography (MUDT) imaging algorithm is proposed for a fixed array MWT system. The MWT system is integrated into the industrial heating unit HEPHAISTOS to estimate the moisture distribution in a polymer foam. In addition, a technique is presented to retrieve the electrical properties of the targets using reconstructed information from MUDT and by investigating the singular values of multistatic scattering data. Through numerical and experimental data for the considered moisture scenarios, the MUDT approach is tested, and its comparison with the UDT approach is shown. Reconstructed results show that in comparison to UDT, the MUDT approach: 1) eliminates the need for mechanical scanning; 2) provides aliasing-free images by following Nyquist sampling criteria; and 3) can resolve multiple targets in the imaging media with significant improvement in the spatial resolution that further augments in the correct retrieval of the dielectric constants of the target. [ABSTRACT FROM AUTHOR]

In today’s ever-developing electronic world, tremendous efforts are being made to develop highly efficient microwave absorbing materials with thin thickness and broad bandwidth while maintaining a simple manufacturing procedure. Since so much study is devoted to structural and component designing of microwave absorbers, the mechanism of microwave absorption still has potential for improvement. It is critical to have fundamental mechanistic knowledge of microwave absorption by absorbing materials with diverse types of geometries and components. It is also important to explore the influence of different physical/chemical characteristics of absorbing materials on microwave absorption performance. The methodical and thorough examination of these issues may provide internal information on the interaction of the microwave absorber’s components and subsequent structural design. Therefore, in this article, by taking cobalt (Co) as the base material, an attempt has been made to comprehend the microwave absorption mechanism of Co milled at various times, which results in changes in particle size and morphology. A significant change in the microwave absorption of Co has been observed with changed morphology after milling. The enhancement in microwave absorption characteristic of milled Co with the addition of different wt% of multiwall carbon nanotubes (MWCNTs) has also been analyzed. MWCNTs in milled Co increase interfacial polarization and multiple reflections in the sample, resulting in microwave attenuation. The prepared Co/MWCNTs composite samples yielded excellent bandwidth with low coating thickness. The effective bandwidth of 10 GHz (3.73–13.73 GHz) was obtained for the composite of 20 h wet-milled Co and 2 wt% MWCNTs, with a coating thickness of 2.5 mm. To further enhance the microwave absorption bandwidth and reduce coating thickness, a genetic algorithm (GA)-assisted multi-layering approach has been implemented for designing triple-layer microwave absorbers. The optimized triple-layer result for combination 5W4–10W1–20W2 shows an effective bandwidth of 13.65 GHz (4.35–18 GHz), with an overall coating thickness of 2.31 mm. [ABSTRACT FROM AUTHOR]

Range migration (or range focusing) techniques are widely used in optical, acoustic, and microwave real-time image reconstruction methods. They have been successfully applied to far-field 3-D imaging where they rely on plane-wave assumptions, which ignore the data amplitude variation over the acquisition aperture. The accuracy of the plane-wave assumption, however, quickly degrades in close-range imaging, where amplitude variations are significant and where the range to the target is on the order of the range sampling step. Here, we present a range-focusing method of improved accuracy, which is applicable to both far-zone and close-range monostatic radar. It refocuses the system point-spread function (PSF) to any range location, taking into account both magnitude and phase changes. The approach can be applied with any Fourier-based imaging algorithm utilizing the Lippmann–Schwinger equation as the underlying scattering model. Here, it is validated through examples based on simulated and measured data where the images are reconstructed with quantitative microwave holography (QMH). QMH employs measured PSFs to achieve quantitative imaging in real-time. The proposed range-migration method is applicable with measured PSFs, too, leading to reduced system-calibration effort and the ability to focus an image at any desired range. [ABSTRACT FROM AUTHOR]

The electromagnetic (EM) resonator-based sensor has a well-known high sensitivity for crack inspection. The nonuniform field distribution inside the resonator, however, causes an associated position-dependent sensitivity phenomenon. This article is intended to address the accurate problem of EM resonator-based sensors. Instead, a methodology based on the traveling wave is first explored. A transmission line-based sensor is implemented for accurate crack depth evaluation. The sensitivity is maintained by tightly bounded the magnetic field in the cross Section of the transmission line through loading the high dielectric material. The accuracy is naturally improved by the uniform field distributed along the propagation direction. An equivalent circuit is modeled based on the transmission line theory to validate the sensing principle. For a wide range of crack width, both simulation and measurement results demonstrate that the detection sensitivity of crack depth is up to 1.893 GHz/mm. The error bar caused by the variation of crack position can be controlled in 0.166 GHz. The performance of multiple crack detection has been verified for the proposed transmission line-based sensor at the same time. This article paves a way to extend the local structural health monitoring based on EM resonators to the large area structural health monitoring. [ABSTRACT FROM AUTHOR]

Objective: Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology. Methods: Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance. Video data were constructed using beamforming. Results: The location of the femoral artery could successfully be resolved, and a distinct arterial pulse wave was discernable. Cardiac activity was imaged at locations corresponding to the heart, but image quality was insufficient to identify individual anatomical structures. Static and differential imaging of the femur bone proved unsuccessful. Conclusion: Using radar chip technology and an imaging approach, cardiovascular activity was detected in the body, demonstrating first steps towards biomedical dynamic microwave imaging. The current portable and modular system design was found unsuitable for static in-body imaging. Significance: This first proof of concept demonstrates that radar-on-chip could enable cardiovascular imaging in a low-cost, small and portable system. Such a system could make medical imaging more accessible, particularly in ambulatory or long-term monitoring settings. [ABSTRACT FROM AUTHOR]

There is a rapid trend in various industries to replace the metallic pipes by nonmetallic ones. This is due to the certain properties, such as high strength, lightweight, resilience to corrosion, and low cost of maintenance for nonmetallic pipes. Despite the abovementioned advantages, nonmetallic pipes are still affected by issues, such as erosion, defects, damages, cracks, holes, delamination, and changes in the thickness. These issues are typically caused due to the manufacturing process, type of carried fluid composition, and flow rate. If not examined well, these issues could lead to disastrous failures caused by leakages and bursting of the pipes. To prevent such major failures, it is extremely important to test the pipes periodically for an accurate estimation of their thickness profile. In this article, we propose a nondestructive testing (NDT) technique, based on near-field microwave holography, for identifying the fluid carried by a nonmetallic pipe and estimating the pipe’s thickness profile. Identifying the carried fluid helps improve the thickness profile estimation. The performance of the proposed techniques will be demonstrated via simulations and experiments. [ABSTRACT FROM AUTHOR]

This work proposes a novel deep learning (DL) framework to solve the electromagnetic inverse scattering (EMIS) problems. The proposed framework integrates the complex-valued deep convolutional neural network (DConvNet) into the supervised descent method (SDM) to realize both off-line training and on-line “imaging” prediction for EMIS. The offline training consists of two parts: 1) DConvNet training: the training dataset is created, and the proposed DConvNet is trained to realize the EM forward process and 2) SDM training: the trained DConvNet is integrated into the SDM framework, and the average descent directions between the initial prediction and the true label of SDM iterative schemes are learned based on the same dataset in part 1). In the online step, the contrasts (permittivities) reconstruction of scatterers is realized by the SDM iteration process based on learned descent directions, while its forward process is achieved by the trained complex-valued DConvNet. Ultimately, this framework provides a new perspective to integrate the prior information into the EMIS solving process with the maintained accuracy. Unlike the conventional SDM, the novel proposed framework can significantly shorten the computation and realize the real-time imaging. Various numerical examples and discussions are provided to demonstrate the efficiency and accuracy of the proposed novel framework. [ABSTRACT FROM AUTHOR]

In order to map the migration and introduction of farming into Europe during the seventh and sixth millennia Before Common Era, archeologists have made a connection between the study of pottery and farming migration. We are interested here in the classification of pottery into coiling and spiral types based on their manufacturing techniques. To distinguish between these two techniques, we look for the lines formed by air bubbles embedded in the pottery samples. Current methods make use of bulky systems, such as computerized tomography scanners or synchrotrons. Microwave acquisition and processing offer an interesting alternative, due to the possibility to have compact and portable systems. In this article, we investigate the classification of pottery based on low-terahertz measurements in the D-band. We process the measurements with 3-D fast Fourier transform. The resulting matrix is classified with an artificial neural network, multilayer perceptron, which is optimized with the gray wolf optimizer, a bioinspired algorithm. The first results show that the accuracy reaches up to 99% using all the acquired spatial and frequency measurements. Then, we optimize the millimeter-wave (mm-Wave) measurement system with a critical criterion on accuracy in two different scenarios. In the first scenario, we reduce the spatial acquisition but maintain the wideband operation and the results show that the accuracy is between 85% and 96%. In the second one, we reduce the spatial acquisition and use a single frequency. For this second scenario, we achieve a classification accuracy, which is between 77% and 100%. [ABSTRACT FROM AUTHOR]

This work presents the design and numerical assessment of a novel microwave imaging (MWI) system, capable of providing a full 3-D image of food/beverage products content in order to disclose the possible presence of physical contaminants, such as plastic fragments. The system here presented exploits the dielectric contrast between the food content and possible intrusions at microwave frequencies; it is based on an antenna array architecture inspecting the items in motion along a conveyor belt without interrupting the production process. The inversion problem is solved by means of linearization, assuming the viability of the Born approximation thanks to the localized intrusions, and regularization, based on the singular value decomposition of the discretized scattering operator. Furthermore, an algorithm, to balance the illumination of the considered scenario due to the nonuniform radiation of the employed antennas, is presented to enhance imaging. The system is first assessed considering an ideal case and then extended to a more realistic approach, for two different kinds of food products, with completely different dielectric properties and considering the performance of existing instrumentation for the purpose. The obtained results lay the foundations for the realization of an actual prototype. [ABSTRACT FROM AUTHOR]

In this article, an innovative approach to microwave imaging, which combines a qualitative imaging technique and deep learning (DL), is presented. The goal is to develop a tool for reliable and user-independent retrieval of the shape of unknown targets from the knowledge of the scattered fields. Qualitative imaging methods are powerful inverse scattering tools, as they provide morphological information in real time. However, their outcome is a continuous map, which has to be hard-thresholded to clearly identify the targets. This thresholding unavoidably results in case-dependent, often user-biased, results. To deal with this issue, a DL approach, based on a physics-assisted deep neural network, is proposed to automatically classify image pixels, i.e., to generate binary masks, separating the targets (foreground) from the background. In particular, the proposed network binarizes the output of a qualitative imaging inversion technique known as the orthogonality sampling method. For the sake of comparison, a DL method is also exploited, which generates the binary masks directly from the scattered fields without any qualitative imaging aid. A quantitative assessment of the performances of both methods and a test on experimental data are provided. [ABSTRACT FROM AUTHOR]

As a rapidly developing novel electromagnetic imaging technique, microwave-induced thermoacoustic tomography (MITAT) has found many applications and attracted tremendous research interest. Using sparse data to reconstruct images is very challenging for MITAT. This work proposes a novel deep-learning-enabled MITAT (DL-MITAT) modality to address the sparse data reconstruction problem and applies it in breast cancer detection. The applied network is a domain transform network called feature projection network (FPNet) + ResU-Net. Detailed structure and implementation method of the network is described. We conduct both simulation and ex vivo experiments with breast phantoms to test the validity of the DL-MITAT approach. The obtained images given by the trained network exhibit much better quality and have much less artifacts than those obtained by a traditional imaging algorithm. We show that only 15 measurements can still reliably recover an image of the breast tumor for both full-view and limited-view configurations in ex vivo experiments. We also provide detailed discussions on the capability and limitations of the proposed scheme. This work presents a new paradigm for MITAT based on sparse data and can be applied in all related applications of MITAT, including biomedical imaging, nondestructive testing, and therapy guidance. [ABSTRACT FROM AUTHOR]

Convolutional neural networks to achieve joint inversion of microwave and ultrasonic data for breast imaging are investigated. Source and field quantities, obtained via backpropagation, are used as inputs. A multistream structure is employed to benefit from data of different modalities. The network outputs the distribution maps of electric and acoustic parameters directly to achieve real-time imaging. Apart from the regression task, a multitask learning strategy is used with a classifier that associates each pixel to a tissue type to yield a segmentation image. Weighted loss is used to assign a higher penalty to pixels in tumors when wrongly classified. Comparisons are carried out between different network structures with the same datasets. The prediction results of the networks are evaluated by Intersection over Union for segmentation results and relative error of retrievals. The simulations on breast phantoms extracted from a dedicated repository show that, with both microwave and ultrasonic data, the network can provide a proper estimate of the breast structure and detection of small tumors. Meanwhile, multitask learning improves the regression results, and multistream input helps to exploit data from different modalities. [ABSTRACT FROM AUTHOR]

The computationally efficient solution of fully nonlinear microwave inverse scattering problems (ISPs) is addressed. An innovative system-by-design (SbD)-based method is proposed to enable, for the first time to the best of the authors’ knowledge, an effective, robust, and time-efficient exploitation of an evolutionary algorithm (EA) to perform the global minimization of the data-mismatch cost function. According to the SbD paradigm as suitably applied to ISPs, the proposed approach is found on: 1) a smart reformulation of the ISP based on the a priori information on the imaged targets for defining a minimum dimensionality and representative set of degrees of freedom (DoFs) and 2) the artificial intelligence (AI)-driven integration of a customized global search technique with a digital twin (DT) predictor based on the Gaussian process (GP) theory. Representative numerical and experimental results are provided to assess the effectiveness and the efficiency of the proposed approach also in comparison with competitive state-of-the-art inversion techniques. [ABSTRACT FROM AUTHOR]

In order to conduct the research of machine learning (ML)-based microwave breast imaging (MBI), a large number of digital dielectric breast phantoms that can be used as training data (ground truth) are required but are difficult to be achieved from practice. Although a few dielectric breast phantoms have been developed for research purpose, the number and the diversity are limited and are far inadequate to develop a robust ML algorithm for MBI. This article presents a neural network method to generate 2-D virtual breast phantoms that are similar to the real ones, which can be used to develop ML-based MBI in the future. The generated phantoms are similar but are different from those used in training. Each phantom consists of several images with each representing the distribution of a dielectric parameter in the breast map. A statistical analysis was performed over 10 000 generated phantoms to investigate the performance of the generative network. With the generative network, one may generate an unlimited number of breast images with more variations, so the ML-based MBI will be more ready to deploy. [ABSTRACT FROM AUTHOR]

Objective: To develop a new class of emulsions using a protein-based emulsifier as the coupling fluid for microwave imaging systems. Methods: In this paper, we provide a theoretical basis for engineering shelf-stable dielectric fluids, a step-by-step formulation method, and measurements of complex dielectric properties in the frequency range of 0.5-3 GHz, which can be applicable for many of the recent microwave imaging systems. Results: This medium was primarily designed for long-term stability while providing a controllable range of complex dielectric permittivities given different fractions of its constituents. Consequently, this emulsion shows dielectric stability in open air throughout a 7-day experiment and temperature insensitivity over the range of 0 ${\,^\circ }$ C to 60 ${^\circ }$. Conclusions: This control over dielectric permittivity enables formulations that tune the background-to-target contrast to the linearizable regime of iterative inverse scattering algorithms. Accordingly, the emulsion conductivity can also be controlled and reduced to maintain the required signal-to-noise ratio within the dynamic range of the imaging system. The new formulation overcomes the practical challenges of engineering coupling fluids for microwave imaging systems, e.g., temporal stability, non-toxic, low sensitivity to temperature variation, and easy formulation from readily available and inexpensive materials. Significance: The achieved properties associated with this new fluid are of particular benefit to microwave imaging systems used in thermal therapy monitoring. [ABSTRACT FROM AUTHOR]

The effect of nanoparticles (NPs) on microwave magnetoresistance in MgO/CoFeB(free)/Ta/CoFeB(fixed)/MgO)/Ta platform and the effect of individual NP on free CoFeB ferromagnetic layer magnetization have experimentally been revealed and analyzed. The area of the platform where magnetization is inverted under scattering field of NPs has been observed. Micromagnetic modeling of dipole magnetic interaction confirms the existence of such areas in “NP-platform” system. The increase in critical field of the platform and the change of resistance under scattering field on NPs have experimentally been revealed. [ABSTRACT FROM AUTHOR]

The derivation of microwave land surface emissivity (MLSE) under various weather conditions from the microwave radiometer plays a crucial role in acquiring land surface and atmospheric parameters. Nevertheless, currently, most existing studies mainly focus on the clear-sky scenarios due to a lack of cloudy-sky land surface temperature (LST) and uncertainties in simulating the scattering and emission properties of atmospheric hydrometeors. Under this background, with satellite observations and the random forest (RF) model, this study proposes a method to estimate the MLSE under cloudy skies. First, clear-sky MLSEs with satisfactory accuracy are retrieved by using the brightness temperatures (BTs) from the Advanced Microwave Scanning Radiometer-Earth sensor, LSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS), and atmospheric profiles from the ERA5 reanalysis. Then, the relation among the clear-sky MLSE and related impact factors is built with the RF and extended to the cloudy-sky environment for generating all-weather MLSEs with a 0.25°. The results show that the input datasets present a considerable impact on the calculation of instantaneous MLSE, and a 5.73 K bias of ERA5 LST may generate a 0.014–0.021 error in the MLSE from 6.9- to 89-GHz horizontal polarization, while the impacts of BT and profile uncertainties on the MLSE are smaller. The retrieved clear-sky MLSE is coincident with the existing MLSE for the spatiotemporal variations, and there is an average difference range from −0.035 to 0.035 in January 2008. Meanwhile, the constructed RF model can successfully apply to cloudy-sky status and recover the MLSE image gaps affected by cloud contamination. [ABSTRACT FROM AUTHOR]

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in the azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with the conventional SAR imaging algorithms, two major problems should be considered: 1) the scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo and 2) the imaging of the MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center (ASC) model, this article analyzes the target scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal subaperture division algorithm is proposed to guarantee the coherence of each subaperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this article proposes a motion error estimation algorithm based on subimage registration, where the full-aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing. [ABSTRACT FROM AUTHOR]

This work presents a breast cancer detection modality using a metasurface as the imaging medium and a microwave source. The fundamental concept is to record the electromagnetic energy, which, when transmitted through the breast, scatters differently according to the variation in the electrical properties of the breast tissue. The energy captured by the metasurface forms an impression image of the breast under test. This impression is used as a detection tool to determine the presence of a tumor inside the breast. A convolution neural network (CNN) is applied to enhance detection accuracy and provide quantitative data about tumors when present. The CNN results show the possibility of finding a tumor with a 10 mm diameter and distinguish between healthy and unhealthy breasts with 93% accuracy while determining the size and the size with location with an accuracy of 91% and 74%, respectively. [ABSTRACT FROM AUTHOR]

When using the statistical inversion framework in microwave tomography (MWT), generally, the real and imaginary parts of the unknown dielectric constant are treated as uncorrelated and independent random variables. Thereby, in the maximum a posteriori estimates, the two recovered variables may show different structural changes inside the imaging domain. In this work, a correlated sample-based prior model is presented to incorporate the correlation of the real part with the imaginary part of the dielectric constant in the statistical inversion framework. The method is used to estimate the inhomogeneous moisture distribution (as dielectric constant) in a large cross section of polymer foam. The targeted application of MWT is in industrial drying to derive intelligent control methods based on tomographic inputs for selective heating purposes. One of the features of the proposed method shows how to integrate lab-based dielectric characterization, often available in MWT application cases, in the prior modeling. The method is validated with numerical and experimental MWT data for the considered moisture distributions. [ABSTRACT FROM AUTHOR]

Computational microwave imaging (CMI) based on the frequency diversity metasurface apertures (FDMAs) is an emerging technology and has attracted wide attention. FDMA-based CMI (FDMA-CMI) can be considered as microwave compressive sensing imaging with the frequency diversity pattern of the FDMA being the sensing matrix and solved by sparse signal reconstruction algorithms. However, the imaging quality is affected by the sensing matrix error and off-grid error seriously. In this article, we propose a novel algorithm for FDMA-CMI, referred to as off-grid sparse Bayesian learning (SBL) method based on sinc interpolation (OGSISBL), by taking both the off-grid error and sensing matrix error into account. First, we establish the measurement model with both the off-grid error and sensing matrix error. Specifically, the off-grid error is represented as a set of parameters to be estimated in the measurement model, and the sensing matrix error is represented as the amplitude–phase drift of the transceiver channels of the imaging system due to the principle of the FDMA. Then, under the framework of the SBL, a robust imaging algorithm OGSISBL is developed via the variational Bayesian expectation maximization (VBEM), which can not only recover the amplitude and position of the return of the scattered, but also simultaneously calibrate the amplitude–phase drift of the transceiver channels and the off-grid error. The performance of the proposed algorithm is evaluated by both the simulation data and the measured data collected by the self-designed experimental FDMA-CMI system, and the results validate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the single channel algorithm (SCA) is first applied to Soil Moisture and Ocean Salinity (SMOS) brightness temperatures at vertical polarization (${\mathrm{ TB}}_{V}$) to estimate SM at coarse resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5°, and 52.5°)-adaptive calibration of single scattering albedo ($\omega$) and soil roughness ($h_{s}$) parameters. These effective parameters are used together with fine-scale multiangular Sentinel-1 backscatter in a single-pass active–passive downscaling approach to estimate ${\mathrm{ TB}}_{V}$ at fine scale (1 km) for each SMOS incidence angle. These ${\mathrm{ TB}}_{V}\mathrm {s}$ are finally inverted to obtain the corresponding high-resolution SM maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of $\omega $ and a decreasing trend of $h_{s}$ with SMOS incidence angle, with almost no variability of $\omega $ across land cover types. The active–passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fine ${\mathrm{ TB}}_{V}$ maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution SM maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well with in situ measurements. Our results indicate that model-based microwave approaches to estimate SM can be adequately adapted to account for the incidence angle diversity of planned missions, such as Copernicus Microwave Imaging Radiometer (CIMR), Radar Observing System for Europe in L-band (ROSE-L), and Sentinel-1 next generation. [ABSTRACT FROM AUTHOR]

Due to the minimum antenna area constraints of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical challenges limiting HRWS imaging in spaceborne SAR. Inspired by the cocktail party effect, an advanced scheme based on blind source separation (BSS) is put forward to suppress the range ambiguity of spaceborne SAR in this article. In the scheme, multiple subantennas are used to collect multiple echo datasets, which are given different patterns. In this way, the echo signal of the desired region and that of the ambiguous region are weighted using different coefficients. During postprocessing, a detailed flow with the BSS algorithm is proposed to separate the desired echoes from those datasets. To verify the scheme, point target and scene simulations based on GF-3 are carried out. The results indicate that the range ambiguity can be reduced by more than 15 dB using the proposed scheme, the processing flow is effective, and the scheme has a good performance without increasing the complexity of the system design under the current SAR system conditions. The scheme has the potential to be adopted in future spaceborne SAR missions. [ABSTRACT FROM AUTHOR]

The uncertainty of passive microwave retrievals of snowfall is notoriously high where high-frequency surface emissivity is significantly reduced and varies markedly in response to the changes in snowpack physical properties. Using the dense media radiative transfer theory, this article studies the potential effects of terrestrial snow-cover depth, density, and grain size on high-frequency channels 89 and 166 GHz of the radiometer onboard the Global Precipitation Measurement (GPM) core satellite, which are commonly used to capture snowfall scattering signals. Integrating the inference across all feasible grain sizes, ranges of snowpack density and depth are identified over which snowfall scattering signatures can be time-varying and potentially obscured. Using ten years of reanalysis data, the seasonal vulnerability of snowfall retrievals to the changes in snowpack emissivity in the Northern Hemisphere is mapped in a probabilistic sense and connections are made with the uncertainties of the GPM passive microwave snowfall retrievals. It is found that among different snow classes, relatively light Arctic tundra snow in fall, with a density below 260 kg m−3, and shallow prairie snow during the winter, with a depth of less than 40 cm, can reduce the surface emissivity and obscure the snowfall passive microwave signatures. It is demonstrated that during winter, the highly vulnerable areas are over Kazakhstan and Mongolia with taiga and prairie snow. In the fall, these areas are largely over tundra and taiga snow in north of Russia and the Arctic Archipelagos as well as prairies in Canada and the Great Plains in the United States. [ABSTRACT FROM AUTHOR]

We present a new method for determining the complex dielectric permittivity profile of 3-D inhomogeneous dielectric objects from measurements of the scattered electric field vectors in the frequency domain. The method is formulated as a minimization of a cost function defined in terms of electric field integral equations known as the object and data equations. Instead of an unknown object function containing the electromagnetic parameters of the dielectrics, the contrast sources induced within the scatterers are designated as the unknowns of the inversion scheme to avoid solving the forward scattering problem at each step. Later, the minimization of the cost function is achieved via a limited-memory quasi-Newton scheme, based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) formula, which iteratively updates the Hessian matrix estimation. The numerical results with the simulated and experimental scattered electric fields demonstrate that the presented method is capable of reconstructing scatterers with complex shapes. [ABSTRACT FROM AUTHOR]

The Fourier-space scattered-power mapping (F-SPM) is proposed as a computationally efficient alternative to the original real-space SPM method for real-time quantitative image reconstruction with an emphasis on close-range and near-field applications. Similar to SPM, F-SPM can employ either the Born or the Rytov data approximations in a linear scattering model, resulting in two distinct algorithms. However, to exploit the complementarity of the two approximations, a strategy is proposed for their combined use in a single inversion process. The combined Born–Rytov F-SPM method consistently improves the image quality in comparison with the images generated when using either approximation separately. The improvement is most significant when the limitations of either the Born or the Rytov approximations are violated. In the cases where neither or both of these limitations are violated, the images are of comparable quality to those generated by the standalone algorithms. The proposed Born–Rytov F-SPM algorithm is verified and compared to the standalone Born-based F-SPM and Rytov-based F-SPM in examples utilizing simulated and measured data. The gain in computational speed compared to the original real-space SPM is also demonstrated. [ABSTRACT FROM AUTHOR]

The clinical use of microwave tomography (MT) requires addressing the significant mismatch between simulated environment, which is used in the forward solver, and real-life system. To alleviate this mismatch, a calibrated tomography, which uses two homogeneous calibration phantoms and a modified distorted Born iterative method (DBIM), is presented. The two phantoms are used to derive a linear model that matches the forward solver to real-life measurements. Moreover, experimental observations indicate that signal quality at different frequencies varies between different antennas due to inevitably inconsistent manufacturing tolerance and variances in radio-frequency chains. An optimum frequency, at which the simulated and measured signals of the antenna present maximum similarity when irradiating the calibrated phantoms, is thus calculated for each antenna. A frequency-division DBIM (FD-DBIM), in which different antennas in the array transmit their corresponding optimum frequencies, is subsequently developed. A clinical brain scanner is then used to assess performance of the algorithm in lab and healthy volunteers’ tests. The linear calibration model is first used to calibrate the measured data. After that FD-DBIM is used to solve the problem and map the dielectric properties of the imaged domain. The simulated and experimental results confirm validity of the presented approach and its superiority to other tomographic method. [ABSTRACT FROM AUTHOR]

Microwave-induced thermoacoustic imaging (MTAI) is a promising alternative for breast tumor detection due to its deep imaging depth, high resolution, and minimal biological hazards. However, due to the bulky size and complicated system configuration of conventional benchtop MTAI, it is limited to imaging various anatomical sites and its application in different clinical scenarios. In this study, a handheld MTAI system equipped with a compact impedance matching microwave-sono and an ergonomically designed probe was presented and evaluated. The probe integrates a flexible coaxial cable for microwave delivery, a miniaturized microwave antenna, a linear transducer array, and wedge-shaped polystyrene blocks for efficient acoustic coupling, achieving microwave illumination and ultrasonic detection coaxially, and enabling high signal-to-noise ratio (SNR). Phantom experiments demonstrated that the maximum imaging depth is 5 cm (SNR = 8 dB), and the lateral and axial resolutions are 1.5 mm and 0.9 mm, respectively. Finally, three healthy female volunteers of different ages were subjected to breast thermoacoustic tomography and ultrasound imaging. The results showed that the h-MTAI data are correlated with the data of ultrasound imaging, indicating the safety and effectiveness of the system. Thus, the proposed h-MTAI system might contribute to breast tumor screening. [ABSTRACT FROM AUTHOR]

Imaging objects with high relative permittivity and large electrical size remains a challenging problem in the field of inverse scattering. In this work, we present a phaseless inverse scattering method that can accurately reconstruct objects even with these attributes. The novelty of the approach is that it uses a high-frequency approximation for waves passing through lossy media to provide corrections to the conventional Rytov approximation (RA). We refer to this technique as the extended phaseless Rytov approximation for low-loss media (xPRA-LM). Simulation and experimental results are provided for RF indoor imaging using phaseless measurements acquired from 2.4-GHz-based WiFi nodes. We demonstrate that the approach provides accurate reconstruction of objects up to relative permittivities of $15+1.5j$ for object sizes greater than 30 wavelengths. Even at higher relative permittivities of up to $\epsilon _{r}=77+ 7j$ , object shape reconstruction remains accurate; however, the reconstruction amplitude is less accurate. To the best of our knowledge, xPRA-LM is the first linear phaseless inverse scattering approximation with such a large validity range and can be used to achieve the potential of RF and microwave imaging in applications such as indoor RF imaging. [ABSTRACT FROM AUTHOR]

Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5–10 km, the earth incidence angle difference is 0–0.4°, and the average brightness temperature change is 0–2 K, but individual pixels may be up to several kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset. [ABSTRACT FROM AUTHOR]

Total precipitable water (TPW) values derived from Constellation Observing System for Meteorology, Ionosphere and Climate-2 (COSMIC-2) are compared with those derived from Special Sensor Microwave Imager Sounder (SSMIS), Global Precipitation Measurement (GPM) Microwave Imager (GMI), and Advanced Microwave Scanning Radiometer-2 (AMSR-2) over the ocean from October 1, 2019 to February 16, 2020. The overall comparison results indicate that TPW values derived from SSMIS, AMSR-2, and GMI have a good correlation and agreement with COSMIC-2 TPW values with the correlation coefficients greater than 0.99 and root mean square (rms) no greater than 2.7 mm. We compare TPW derived from three different microwave radiometers with COSMIC-2 TPW over the subtropical and tropical oceans. The differences illustrate that TPW values derived from three different microwave radiometers are more consistent with COSMIC-2 TPW values over the subtropical ocean than those over the tropical ocean. In addition, we also analyze the relationship between the TPW retrieval accuracy derived from three different microwave radiometers and environmental factors, including cloud, rain rate, wind speed, and surface temperature. The results indicate that four environmental factors have an important influence on the TPW retrieval from three different microwave radiometers. [ABSTRACT FROM AUTHOR]

A novel noncoherent microwave imaging principle with periodical or random radio frequency (RF) illumination is proposed in this article. Implemented with circular array synthetic aperture radar (SAR) frontend and low-complexity signal processing algorithms, the imaging device, called RF camera, achieves some desired properties similar to an optical camera, such as the capability to operate with multiple uncoordinated illuminators. Different from conventional multistatic imaging, the RF camera does not require any knowledge about an illuminator’s location or signal waveform. A static illumination sensor (IS) can be used to provide a reference signal for image reconstruction. With periodical illumination, the RF camera can even operate without IS, but the imaging performance can be improved with IS. With random illumination, the IS is necessary for the RF camera operation, and the imaging distortion can be described by a point blur function. Theoretical analyses on the imaging signal-to-noise ratios are performed under different RF camera operation modes. Simulation and experimental tests are conducted using 77-GHz millimeter wave frequency to verify the noncoherent imaging principle and its performance. [ABSTRACT FROM AUTHOR]

Microwave and millimeter-wave measurements acquire total-field responses from measurements, yet imaging algorithms instead require the data in the form of scattered-field responses. Two approaches exist for the extraction of the scattered-field data from the total-field responses, namely, the Born and the Rytov data approximations. It is well known that, depending on the target’s size, contrast, and structural complexity, one approximation can achieve an improved accuracy over the other. Yet, the Rytov approximation is rarely used in microwave and millimeter-wave imaging, likely due to phase-unwrapping problems occurring in the case of strongly heterogeneous electrically large targets. Here, we propose a method to utilize the Born and the Rytov approximations simultaneously in a single inversion step for real-time imaging with quantitative microwave holography (QMH). We show through examples with simulated and experimental data that in near-field imaging scenarios, including the imaging of a breast-tissue phantom, there are significant benefits in employing the new method. [ABSTRACT FROM AUTHOR]

A terahertz (THz) CMOS detector array based on multiple chips stitched together is reported. The proposed multichip detector is scalable as the total number of subarray chips is flexible and can be controlled as needed with the help of the modular scheme adopted. Based on the presented multichip technique, a $3\times $ 3 multichip detector array composed of nine subarray chips has been implemented in this work for operation at 300 GHz. With the subarray chips, each with $7\times $ 7 pixels, the complete multichip array comprises $21\times21$ physical pixels. By employing virtual pixels, which are included to compensate for the chip interface area consumed for interchip wire-bonding in this work, THz real-time images with $23\times23$ pixels have been successfully acquired. The distribution of the responsivity and noise equivalent power (NEP) over the multichip array is presented. The responsivity distribution shows the effect of the series resistance of the long bias lines stretched over the multichip array, while the effect is not apparent for NEP. The distribution due to series resistance, as well as other nonuniformities over the pixels and chips due to various causes, can be suppressed with a proper calibration for the images acquired with the multichip array. [ABSTRACT FROM AUTHOR]

Computational microwave imaging with frequency-diverse metasurface (FDM) apertures is an emerging technology. In this article, we establish an experimental FDM microwave imaging system and address the waveform design problem based on the information theory, aiming to enhance the advantages of the FDM imaging. Two waveform design methods based on the different criteria are proposed for the FDM imaging system. The first waveform is designed by maximizing the mutual information between the object and the measured data with the constant transmitted energy, and the second one is designed by minimizing the transmitted energy while the mutual information is not less than a threshold. The performance of the proposed waveform design methods is evaluated by the data gathered by the self-established experimental FDM imaging system. The results show that the proposed waveform design methods are capable of improving the imaging quality or the imaging efficiency of the FDM imaging system. [ABSTRACT FROM AUTHOR]

The assimilation of spaceborne passive microwave measurements often suffers from representativeness errors due to the mismatch between the observation footprints and numerical weather prediction (NWP) model grids. In this article, a new brightness temperature remapping technique based on a deep convolutional neural network (CNN) is proposed to reduce the representativeness error in FengYun-3D (FY-3D) microwave radiation imager (MWRI) observation data assimilation. The remapping technique uses an adapted dataset construction method in which the training data consist of synthetic NWP-model-grid-based MWRI brightness temperature ($T_{B}$) images and synthetic MWRI-observed antenna temperature ($T_{A}$) images. The synthetic $T_{A}$ and $T_{B}$ , which are generated through radiative transfer for TOVS (RTTOV) model and MWRI degradation model, make the network learn the spatial remapping relationship between observation and background $T_{B}$ in data assimilation. In addition, land–sea mask information is input into the CNN to help the network better analyze the coastline area data. The CNN-based remapped MWRI observation data are evaluated through observation minus background (OMB) diagnosis with the Global/Regional Assimilation and PrEdiction System (GRAPES) four-dimensional variational (4D-Var) system. The experimental results illustrate that the bias and standard deviation of OMB with the CNN-based remapped MWRI observation are quantitatively reduced compared with the raw measurements in GRAPES 4D-Var. [ABSTRACT FROM AUTHOR]

Accurate intensity estimation of tropical cyclone (TC) is important and challenging. Currently, no standardized method is available for estimating the TC intensity, usually in terms of the maximum surface wind speed, using satellite remote sensing data. This article proposes a multifeature distribution learning (MFDL) model that uses satellite microwave brightness temperatures to estimate the TC intensity. The MFDL model uses the brightness temperatures of Advanced Technology Microwave Sounder (ATMS) to generate the synthetic temperature anomaly fields of TCs at different pressure levels. These 3-D temperature anomaly data are trained by MFDL to establish the relationship between anomaly fields and TC intensity. MFDL treats the TC intensity estimation as a probability distribution of wind speed, instead of as a regression problem as in the conventional TC intensity estimation methods. In this study, 964 TCs occurred in North Atlantic (NA) and North Pacific (NP) from 2012 to 2019 are used for training and testing. The simulation results suggest that estimation accuracy is greatly improved using the preprocessed 3-D TC anomaly data. The MFDL network achieves the average mean absolute errors (MAEs) of 4.37 m/s for NA TCs and 4.92 m/s for NP TCs in the years 2018 and 2019. The results show that the MFDL network and ATMS temperature-based anomaly can be a promising means for TC intensity estimation in operational weather forecast. [ABSTRACT FROM AUTHOR]

A semiquantitative technique for imaging of high-power millimeter wave (MMW) beam using visible light from the plasma of an MMW-induced gas breakdown, which was initiated by a metal-dielectric (MD) screen, is discussed. The design of the gas breakdown sensor for imaging of the wave beam is presented. A plane MD screen was placed in a shallow airtight chamber with a transparent window of a thin mylar film. The chamber was filled with gas at a near atmospheric pressure. An MMW-induced breakdown in air, nitrogen, helium, and krypton, initiated by an MD screen, has been experimentally studied. In the experiments, a high-power 250-GHz pulsed gyrotron served as the source of MMWs. In a chamber filled with noble gases, bulk plasma was formed at an MMW intensity above the gas breakdown threshold. A single-pulse wave beam was imaged using a profile of the flux density of light from the breakdown plasma in helium and krypton. It was shown that using the presented technique, one can determine the position of the intensity maxima and the beam profile of an MMW beam in the range from one to two threshold intensities of an MMW-induced gas breakdown initiated by an MD screen. [ABSTRACT FROM AUTHOR]

Microwave-induced thermoacoustic imaging (MTAI) has been widely used in biomedical science, and has the potential as an auxiliary measure for clinical diagnosis and treatment. Recently, there are increasing interests in using ultrashort microwave-pumped thermoacoustic imaging techniques to obtain high-efficiency, high-resolution images. However, the traditional imaging system can only provide uniform radiation in a relatively small area, which limits their large field of view in clinical applications (such as whole-breast imaging, brain imaging). To address this problem, we propose an ultrashort pulse microwave thermoacoustic imaging device with a large size aperture antenna. The system can provide a microwave radiation area of 40 cm × 27 cm and a uniform imaging view of 14 cm × 14 cm. With 7 cm imaging depth and a 290 μm resolution. The practical feasibility of the system for breast tumor screening is tested in phantoms with different shapes and in an ex vivo human breast tumor which is embedded in the excised breast of an ewe (π × 5 cm × 5 cm). The tumor can be identified with a contrast of about 1:2. The results demonstrate that the dedicated MTAI system with the uniform large field of view, high imaging resolution, and large imaging depth have the potential for clinical routine breast screening. [ABSTRACT FROM AUTHOR]