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In this paper, a deep learning based time-domain inversion method is proposed to reconstruct high-contrast scatterers from the measured electromagnetic fields. The scatterers investigated in this study include four kinds of geometry shapes, which cover the arbitrary geometrical shapes, handwritings and lossy medium. After being well trained, the performance of the proposed method is evaluated from the perspective of accuracy, noise interference, and computational acceleration. It can be proven that the proposed framework can realize high-precision inversion in several milliseconds. Compared with typical reconstruction methods, it avoids the iterative calculation by utilizing the parallel computing ability of GPU and thus significantly reduce the computing time. Besides, the proposed method has shown the potential to be applied in practical scenarios with experimental results. Herein, it is confident that the proposed method has the potential to serve as a new path for real-time quantitative microwave imaging for various practical scenarios. In the end, the limitation of the method is also discussed. [ABSTRACT FROM AUTHOR]

This paper presents a real-time and non-contact dual-mode embedded impulse-radio (IR) ultra-wideband (UWB) radar system designed for microwave imaging and vital sign applications. The system is fully customized and composed of three main components, an RF front-end transmission block, an analog signal processing (ASP) block, and a digital processing block, which are integrated in an embedded system. The ASP block enables dual-path receiving for image construction and vital sign detection, while the digital part deals with the inverse scattering and direct current (DC) offset issues. The self-calibration technique is also incorporated into the algorithm to adjust the DC level of each antenna for DC offset compensation. The experimental results demonstrate that the IR-UWB radar, based on the proposed algorithm, successfully detected the 2D image profile of the object as confirmed by numerical derivation. In addition, the radar can wirelessly monitor vital sign behavior such as respiration and heartbeat information. [ABSTRACT FROM AUTHOR]

Microwave sensing is envisioned to be a reliable imaging technique for detecting and monitoring brain tumours. It uses low-power, nonionized microwave signals to visualize the interior of the living tissues. This study automatically exploits categorization of brain tumours using a deep neural network called the YOLOv5 l algorithm in non-invasive microwave images. A modified antipodal structure (including parasitic patch between flares) that operates over the wideband is proposed for the collection of scattering parameters. The staircase parasitic patch located at the centre of the radiator provides the improved radiation performance at low frequencies. Subsequently, a simulation environment was modelled using CST Microwave Studio 2020, which contained a data acquisition system using a designed microwave antenna and head phantom. Next, the image reconstruction is implemented MATLAB software. The detection and classification of tumours were performed using the YOLOv5l model. A dataset with 500 image samples was formed by pre-processing, which was divided into training (70%), testing (15%) and validation (15%). Then, augmentation is used to create final dataset with 1000 training images. The model was successful in locating an early-stage brain tumour with a size of 5 mm. The YOLOv5 model automatically detected tumour (s) and categorized them as benign tumour or malignant tumour based on a predicted bounding box with an accuracy of 91.5%. [ABSTRACT FROM AUTHOR]

In-Orbit Demonstration (IOD) missions represent flight opportunities to acquire data on spacecraft and space environment and to demonstrate new techniques at system, subsystem, and payload level. Realizing this type of mission means accepting a greater level of risk. However, when very compact and relatively simple platforms like CubeSats are adopted, this becomes admissible since costs can be kept low. Distributed Synthetic Aperture Radar (SAR) is a novel Earth Observation (EO) technique for microwave imaging. It uses multiple cooperating receivers and suitable processing to enable performance and products that exceed those of the single components of the system. RODiO (Radar for earth Observation by synthetic aperture Distributed on a cluster of CubeSats equipped with high-technology micro-propellers for new Operative services) is an IOD of the distributed SAR concept. The mission is based on four, 16 U, CubeSats, that embark a receiving-only X-band SAR instrument able to collect bistatic echoes exploiting a monostatic SAR as an opportunity illuminator, i.e PLATiNO-1 mission. Distributed SAR demonstration is thus pursued in RODiO combining the principles of both multistatic and bistatic SAR. The paper, moving from mission goals, individuates different families of EO products that RODiO can demonstrate, e.g. in the contest of ground motion, digital elevation models, and ship detection. The main result is the definition of the RODiO system requirements for product generation that drive satellites and payload design. • RODiO is an IOD mission of DSAR principle based on four 16U CubeSat receivers. • The combination of mono and bistatic images enables the generation of new products. • These products provide advantages with respect to the monostatic-only counterparts. • Preliminary performance assessment of multi-baseline products is shown in the paper. • Requirements for product generation have been derived to drive RODiO design. [ABSTRACT FROM AUTHOR]

This paper demonstrates the design steps of a slot-loaded Vivaldi antenna for biomedical microwave imaging applications, showing the influence of the design parameters on the antenna's dimensions and performances. Several antenna miniaturization techniques were taken into consideration during the design: reduction in the electromagnetic wavelength by using a high-permittivity substrate material (relative permittivity ϵ r = 10.2 ), the placement of the antenna inside a coupling medium ( ϵ r = 23 ), and the elongation of the current path by etching slots on each side of the radiator to reduce the antenna's lowest resonant frequency without increasing its physical dimensions. Moreover, an analysis of different antenna slot design scenarios was performed considering different slot lengths, inclination angles, positions, and numbers. Considering the frequency range of microwave imaging (i.e., about 500 MHz–5 GHz) and the array arrangement typical of microwave imaging, the best design was chosen. Finally, the antenna was fabricated and its performances in the coupling medium were characterized. The simulation and measurement results showed good agreement between each other. In comparison with literature antennas, the one developed in this work shows wide bandwidth and compact dimensions. [ABSTRACT FROM AUTHOR]

This article proposes a modular and passive all-metal lens to achieve near-field focusing with adjustable focus. The proposed lens consists of all-metal units with the phase coverage range exceeding 360°, and the arrangement of units is guided by the phase compensation method. Specifically, using the strategy of module unit synthesis, the arrangement of lens units under different focuses can be assembled arbitrarily, which reduces the production costs by 39.3% and improves the freedom of lens design. The simulation and experimental results show that the lens exhibits excellent focusing properties and freely changes the position of the expected focus (0.30 m–0.75 m). Therefore, the modular all-metal lens designed in this article has the characteristics of high transparency and a high degree of freedom, which can provide low-cost and lightweight solutions for various applications in the field of antennas, such as near-field target detection, microwave imaging, biomedicine, and so on. [ABSTRACT FROM AUTHOR]

We apply subspace migration (SM) for fast identification of a small object in microwave imaging. Most research in this area is performed under the assumption that the diagonal elements of the scattering matrix can be easily measured if the transmitter and the receiver are in the same location. Unfortunately, it is very difficult to measure such elements in most real-world microwave imaging. To address this issue, several studies have been conducted with the unknown diagonal elements set to zero. In this paper, we generalize the imaging problem by using SM to set the diagonal elements of the scattering matrix to a constant. To demonstrate the applicability of SM and its dependence on the constant, we show that the imaging function of SM can be represented by an infinite series of Bessel functions of integer order, antenna number and arrangement, and the applied constant. This result allows us to discover additional properties, such as the unique determination of the object. We also demonstrated simulation results using synthetic data to back up the theoretical result. [ABSTRACT FROM AUTHOR]

Multifocal (MF) and multicentric (MC) breast cancers are more likely to be aggressive than unifocal (UF) breast tumors, and they are linked to high recurrence probability, lymph node metastases, and low survival rates. Metastasis is the process through which cancer cells spread to different body areas. Although the longevity of patients with metastatic breast cancer is high, it is restricted by the growth of brain metastases. Medical imaging techniques have inherent limits. Hence, a precise and sensitive substitution is required. Because microwave imaging is portable, non-ionizing, and affordable, it has become a viable method for the detection of breast cancer. As far as we know, this is the first research aiming at discriminating MF and MC breast cancers from UF ones. This proposal presents a 2 × 2 planar metasurface-based array antenna in the Industrial Scientific Medical frequency band at 2.4 GHz. The proposed array configuration significantly enhanced the gain to 4 dBi rather than a single element at 50 Ω. The geometric arrangement of the four antenna elements is optimized to discriminate the critical stage of breast cancers (UF, MF, or MC). As the proposed structure does not surround the breast, it contributes to a higher degree of comfort than the conventional antenna configurations. The magnitude and phase of S-parameters of the backscattered signals are analyzed in this research.Article Highlights: Proposed a novel antenna configuration for detecting breast cancer. Revealed significant discrimination between tiny, spreadable tumors and large, non-spreadable ones. Introduced a new detection metric for analysis and provided comfort for breast cancer patients. [ABSTRACT FROM AUTHOR]

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1–15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives. [ABSTRACT FROM AUTHOR]

This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC's permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI. [ABSTRACT FROM AUTHOR]

In modern communication systems, ultra-wideband (UWB) technology has garnered substantial attention due to its superior attributes compared to traditional narrowband communication systems. Over the past decade, UWB technology has also found applications in microwave-based imaging systems. This study introduces a simple planar coplanar waveguide-fed circular shape arc slot antenna designed specifically for biomedicine and microwave medical imaging applications. The proposed design is implemented on a 1.6-mm-thick FR4 substrate with a relative permittivity of 4.4 and a loss tangent of 0.0009. The antenna has physical dimensions of 26 mm × 29 mm and achieves an impressive bandwidth of 16.6 GHz, spanning 2.4 to 19 GHz. It exhibits a peak gain of 2.5 dBi and consistent omnidirectional radiation characteristics. Thorough temporal analysis validates the antenna's performance within acceptable limits, which is further affirmed through practical fabrication and testing, demonstrating strong agreement with simulation results. [ABSTRACT FROM AUTHOR]

It has been demonstrated that the MUltiple SIgnal Classification (MUSIC) algorithm is fast, stable, and effective for localizing small anomalies in microwave imaging. For the successful application of MUSIC, exact values of permittivity, conductivity, and permeability of the background must be known. If one of these values is unknown, it will fail to identify the location of an anomaly. However, to the best of our knowledge, no explanation of this failure has been provided yet. In this paper, we consider the application of MUSIC to the localization of a small anomaly from scattering parameter data when complete information of the background is not available. Thanks to the framework of the integral equation formulation for the scattering parameter data, an analytical expression of the MUSIC-type imaging function in terms of the infinite series of Bessel functions of integer order is derived. Based on the theoretical result, we confirm that the identification of a small anomaly is significantly affected by the applied values of permittivity and conductivity. However, fortunately, it is possible to recognize the anomaly if the applied value of conductivity is small. Simulation results with synthetic data are reported to demonstrate the theoretical result. [ABSTRACT FROM AUTHOR]

The linear sampling method is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. Nevertheless, there are many challenges that this method depends on the frequency of operation and it is unable to produce satisfactory results for objects with complex shapes. This paper proposes a hybrid model, which combines conventional linear sampling method and deep learning for the reconstruction of mixed boundary objects. In this approach, the initial approximation of mixed boundary objects derived from linear sampling method serves as the training data for the U-Net based convolutional neural network. The network then learns to correlate this approximation with the corresponding ground truth profiles. Along with the reconstruction of mixed boundary objects, they are also classified as dielectric or conductor, and count of each object type are measured. Furthermore, the low-frequency and high-frequency characteristics of the linear sampling method are analyzed, and its limitations are overcome by combining it with a deep learning approach. The effectiveness of the proposed model is validated using several examples of synthetic and experimental data. The results demonstrate that the proposed method outperforms the conventional Linear sampling method in terms of accuracy.

In this study, a linear approach, linear sampling method (LSM) is used to reconstruct the shape of perfectly electric conductors (PEC) with the help of deep learning as a post-processing technique. In microwave imaging, the LSM is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. However, the output of this method depends on the frequency of operation, the choice of regularization parameter,and it is unable to produce satisfactory results for objects with complex shapes. To overcome this drawback, a deep learning approach is used in this work, which can produce a better output in terms of accuracy, resolution. Here, the rough estimate of the PEC scatterer obtained using LSM is used to train the U-Net based convolutional neural network, which maps this output with the corresponding ground truth profiles. The proposed hybrid model is validated using several examples of synthetic and experimental data.

A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of which is continuously improving and converging as the number of data points increases with the stream of additional measurements. It is shown that the images converge to those obtained with data acquired on a uniformly sampled surface, where the sampling density satisfies the Nyquist limit. The image reconstruction employs a new formulation of the method of scattered power mapping (SPM), which first maps the data into a three-dimensional (3D) preliminary image of the target on a uniform spatial grid, followed by fast Fourier space image deconvolution that provides the high-quality 3D image. [ABSTRACT FROM AUTHOR]

AbstractThe microwave and millimeter-wave (MMW) imaging technology is gaining increasing interest for food inspection. It allows for noninvasive, contactless, and fast scanning capabilities, while being cost-efficient and safe to human. This review paper introduces the fundamentals in the interaction of electromagnetic wave with food materials and the current MMW sensing and imaging systems used for foods. Then we present emerging technologies in MMW imaging for inspecting food quality and safety, aiming to meet the modern food industry’s demand. According to the most recent technological advancements, it is expected that high-performance antenna, ultrawide bandwidth signal generation, nano-scale semiconductor technologies, radio frequency identification with inductance-capacitance resonator, and machine learning could significantly enhance the capabilities of MMW imaging systems for food inspection. [ABSTRACT FROM AUTHOR]

This paper presents an experimental investigation of the effect of clutter on the performance of microwave imaging and target identification using ground-penetrating radar (GPR) for buried objects. Signals collected by the GPR were formed to be B-scan and 3D images to carry out the microwave imaging of buried objects. Poles extracted using the short-time matrix pencil method from a late-time response of the received signal collected at a specific position were exploited to identify buried objects. The experiment was set up by constructing a large box filled with sand where an L-shaped metallic sheet was buried. The GPR was installed along with a 2-dimensional scanner above the box to image and identify the buried metallic sheet. In order to investigate the effect of the clutter on the performance of the GPR-based microwave imaging and target identification, small rocks were arranged on the sand surface, and their density was then varied. The experimental results have shown that clutter density significantly affects the sharpness of the B-scan and 3D images. The rocks result in an additional surface layer in the images. Moreover, the results have also shown that the clutter does not affect the performance of the identification of the buried object. From the results, the poles obtained from the GPR scanning inside and outside the buried object region differed. This confirms that the poles of the buried object should be different from those without the object. The poles of the buried object remain constant, although rocks on the sand surface exist since the clutter affects only the early-time response (not the late-time response). With varying clutter densities, the underlying poles were significantly changed, but this does not affect the identification of the buried object. [ABSTRACT FROM AUTHOR]

In microwave imaging, the adjoint method is widely used for the efficient calculation of the update direction, which is then used to update the unknown model parameter. However, the utilization and the formulation of the adjoint method differ significantly depending on the imaging scenario and the applied optimization algorithm. Because of the problem‐specific nature of the adjoint formulations, the dissimilarities between the adjoint calculations may be overlooked. Here, we have classified the adjoint method formulations into two groups: the direct and indirect methods. The direct method involves calculating the derivative of the cost function, whereas, in the indirect method, the derivative of the predicted data is calculated. In this review, the direct and indirect adjoint methods are presented, compared, and discussed. The formulations are explicitly derived using the two‐dimensional wave equation in frequency and time domains. Finite‐difference time‐domain simulations are conducted to show the different uses of the adjoint methods for both single source‐multiple receiver, and multiple transceiver scenarios. This study demonstrated that an appropriate adjoint method selection is significant to achieve improved computational efficiency for the applied optimization algorithm. [ABSTRACT FROM AUTHOR]

This work presents preliminary experimental results for advancing non-destructive testing methods for detecting steel bars in cement via contactless investigations in the X-band spectrum. This study reveals the field's penetration into cement, extracting insights into embedded bars through scattered data. Applying a quasi-quadratic inverse scattering technique to numerically simulated data yields promising results, confirming the effectiveness and reliability of the proposed approach. In this realm, using a higher frequency allows for the use of lighter equipment and smaller antennas. Identified areas for improvement include accounting for antenna behavior and establishing the undeformed target morphology and precise orientation. Transitioning from powder-based and sand specimens to real, solid, reinforced concrete structures is expected to alleviate laboratory challenges. Although accurately determining concrete properties such as its relative permittivity and conductivity is essential, it remains beyond the scope of this study. Finally, overcoming these challenges could significantly enhance non-invasive testing, improving structural health monitoring and disaster prevention. [ABSTRACT FROM AUTHOR]

A group of algorithms for estimating the current intensity (CI) of typhoons, which use infrared and microwave sensor-based images as the input of the algorithm because it is more skilled than each algorithm separately, are used to create a technique to estimate the typhoon intensity which is known as SATCON. In the current study, an effort was undertaken to assess how well the SATCON approach performed for estimating typhoon intensity throughout the West Pacific basin from year 2017 to 2021. To do this, 26 typhoons over the West Pacific basin were analysed using the SATCON-based technique, and the estimates were compared to the best track parameters provided by the Regional Specialized Meteorological Centre (RSMC), Tokyo. The maximum sustained surface winds (Vmax) and estimated central pressures (ECP) for various 'T' numbers and types of storm throughout the entire year, as well as during the pre-monsoon (March–July) and post-monsoon (July–February) seasons, have been compared. When compared to weaker and very strong typhoons, the ability of the SATCON algorithm to estimate intensity is determined to be rather excellent for mid-range typhoons. We demonstrate that SATCON is more effective in the post-monsoon across the West Pacific basin than in the pre-monsoon by comparing the algorithm results. [ABSTRACT FROM AUTHOR]

The linear sampling method is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. Nevertheless, there are many challenges that this method depends on the frequency of operation and it is unable to produce satisfactory results for objects with complex shapes. This paper proposes a hybrid model, which combines conventional linear sampling method and deep learning for the reconstruction of mixed boundary objects. In this approach, the initial approximation of mixed boundary objects derived from linear sampling method serves as the training data for the U-Net based convolutional neural network. The network then learns to correlate this approximation with the corresponding ground truth profiles. Along with the reconstruction of mixed boundary objects, they are also classified as dielectric or conductor, and count of each object type are measured. Furthermore, the low-frequency and highfrequency characteristics of the linear sampling method are analyzed, and its limitations are overcome by combining it with a deep learning approach. The effectiveness of the proposed model is validated using several examples of synthetic and experimental data. The results demonstrate that the proposed method outperforms the conventional Linear sampling method in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a flexible monopole antenna fed by a coplanar waveguide (CPW) feeding line with a barium titanate (BaTiO3) siliconelastomer impedance matching layer for microwave head imaging applications. The operating frequency bandwidth of the proposed antenna is 614 MHz which is from 0.475 GHz to 1.089 GHz. In biomedical microwave sensing and imaging applications, the major challenge is the high power loss due to reflection between the body and the antenna due to impedance mismatch. Therefore, the proposed BaTiO3 silicon-elastomer composite is designed to have dielectric property of 20 which acts as an impedance matching layer for the monopole antenna. The proposed antenna has dimensions of 70×30×6 mm. The flexibility of the antenna is provided by the use of the silicon elastomer. It has been shown that the power radiated into an artificial head phantom improved by almost 160% as compared to antenna without impedance matching layer. Moreover, the SAR level is 0.0286 W/kg when 1 mW of power is transmitted, which is well below the limit set by the regulation. This makes the antenna suitable for wearable biomedical applications due to its wideband characteristic and improved power penetration into human head. [ABSTRACT FROM AUTHOR]

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The problem of the real-time microwave imaging of small, moving objects from a scattering matrix without diagonal elements, whose elements are measured scattering parameters, is considered herein. An imaging algorithm based on a Kirchhoff migration operated at single frequency is designed, and its mathematical structure is investigated by establishing a relationship with an infinite series of Bessel functions of integer order and antenna configuration. This is based on the application of the Born approximation to the scattering parameters of small objects. The structure explains the reason for the detection of moving objects via a designed imaging function and supplies some of its properties. To demonstrate the strengths and weaknesses of the proposed algorithm, various simulations with realdata are conducted. [ABSTRACT FROM AUTHOR]

Visualizing the near-field distribution of microwave field in a monopole antenna is very important for antenna design and manufacture. However, the traditional method of measuring antenna microwave near field distribution by mechanical scanning has some problems, such as long measurement time, low measurement accuracy and large system volume, which seriously limits the measurement effect of antenna microwave near field distribution. In this paper, a method of microwave near-field imaging of a monopole antenna using a nitrogen-vacancy center diamond is presented. We use the whole diamond as a probe and camera to achieve wide-field microwave imaging. Because there is no displacement structure in the system, the method has high time efficiency and good stability. Compared with the traditional measurement methods, the diamond probe has almost no effect on the measured microwave field, which realizes the accurate near-field imaging of the microwave field of the monopole antenna. This method achieves microwave near-field imaging of a monopole antenna with a diameter of 100 µm and a length of 15 mm at a field of view of 5 × 5 mm, with a spatial resolution of 3 µm and an imaging bandwidth of 2.7~3.2 GHz, and an optimal input microwave phase resolution of 0.52° at a microwave power of 0.8494 W. The results provide a new method for microwave near-field imaging and measurement of monopole antennas. [ABSTRACT FROM AUTHOR]

The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) is an advanced microwave imaging technology that provides all-weather and all-day target information. However, as spaceborne SAR resolution improves, traditional echo signal models based on airborne SAR design become inadequate due to the curved orbit, Earth rotation, and increased propagation distance. In this study, we propose an accurate range model for high-resolution spaceborne SAR by analyzing motion trajectory and Doppler parameters from the perspective of the space geometry of spaceborne SAR. We evaluate the accuracy of existing range models and propose an advanced equivalent squint range model (AESRM) that accurately fits the actual range history and compensates for high-order term errors by introducing third-order and fourth-order error terms while maintaining the simplicity of the traditional model. The proposed AESRM's concise two-dimensional frequency spectrum form facilitates the design of imaging algorithms. Point target simulations confirm the effectiveness of the proposed AESRM, demonstrating significant improvements in fitting accuracy for range histories characterized by nonlinear trajectories. The developed AESRM provides a robust foundation for designing imaging algorithms and enables higher resolution and more accurate radar imaging. [ABSTRACT FROM AUTHOR]

Since 2008, the Yellow Sea has experienced a world's largest-scale marine disasters, known as the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced AI models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 Synthetic Aperture Radar (SAR) images. Most importantly, this study presents a continuous and seamless weekly average green tide coverage dataset with the resolution of 500 m, by integrating high precise daily optical and SAR data during each week during the green tide breakout. The uncertainty assessment of this weekly product shows it is completely consistent with the overall direct average of the daily product (R2=1 and RMSE=0). Additionally, the individual case verification in 2019 also shows that the weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic curve-like characteristics, with an low uncertainty (R2=0.89 and RMSE=275 km2).This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at http://dx.doi.org/10.12157/IOCAS.20240410.002 (Gao et al., 2024). [ABSTRACT FROM AUTHOR]

In this contribution, a wearable microwave imaging system for real-time monitoring of brain stroke in the post-acute stage is described and validated. The system exploits multistatic/multifrequency (only 50 frequency samples) data collected via a low-cost and low-complexity architecture. Data are collected by an array of only 16 antennas moved by pneumatic system. Phantoms, built from ABS material and filled with appropriate Triton X-100-based mixtures to mimic the different head human tissues, are employed for the experiments. The microwave system exploits the differential scattering measures and the Incoherent MUSIC algorithm to provide a 3D image of the region under investigation. The shown results, although preliminary, confirm the potential of the proposed microwave system in providing reliable results, including for targets whose evolution is as small as 16 mL in volume. [ABSTRACT FROM AUTHOR]

Non-equilibrium plasmas at atmospheric pressure are often characterized by optical emission spectroscopy. Despite the simplicity of recording optical emission spectra in plasmas, the determination of spatially resolved plasma properties (e.g., electron temperature) in an efficient way is very challenging. In this study, spatially resolved optical images of a microwave argon plasma jet expanding into the ambient air are recorded over a wide range of wavelengths using a hyperspectral imaging system based on a tunable Bragg-grating imager coupled to a scientific complementary metal–oxide–semiconductor camera. The system's working principle is detailed, along with the necessary post-processing steps. Further analysis of the spatial–spectral data, including the Abel transform used to determine 2D radially resolved spatial mappings, is also presented. Overall, the proposed approach provides unprecedented cartographies of key plasma parameters, such as argon and oxygen line emission intensities, Ar metastable number densities, and argon excitation temperatures. Considering that all these plasma parameters are obtained from measurements performed in a reasonable time, Bragg-grating-based hyperspectral imaging constitutes an advantageous plasma diagnostic technique for detailed analysis of microwave plasma jets used in several applications. [ABSTRACT FROM AUTHOR]

In the medical field, microwave imaging technology has experienced rapid development due to its non-invasive and non-radioactive nature. The confocal algorithm is a method commonly used for microwave breast cancer imaging, with the key objective of removing clutter in images to achieve high-quality results. However, the current methods are facing challenges in removing clutter. In order to reduce the clutter in images, a multiplicative improved coherence factor delay multiply and sum algorithm based on the maximum interclass differencing method is proposed. The algorithm compares the starting and ending moments of tumor signals in different channels to determine whether the tumor-scattered signals in different channels overlap in time. An improved coherence coefficient is obtained by summing the non-overlapping signals and multiplying the time window. The multiplicative improved coherence factor, which is obtained by multiplying the coherence coefficients of the improved multi-pair signals, is then multiplied by the focal point intensity obtained using the delay multiply and sum algorithm to reduce clutter in an image. To evaluate the performance of the proposed algorithm, several low-cost uniform and non-uniform models of human breast and tumor tissue with dielectric properties were prepared for testing. The experimental results show that, compared to the existing algorithm, the proposed algorithm can greatly reduce the clutter in images, with a signal-to-clutter ratio of at least 4 dB higher as well as contrast at least six-fold higher. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for the solution of inverse scattering problems that integrates traditional imaging methods and deep learning. The goal is to image piece-wise homogeneous targets and it is pursued in three steps. First, raw-data are processed via orthogonality sampling method to obtain a qualitative image of the targets. Then, such an image is fed into a U-Net. In order to take advantage of the implicitly sparse nature of the information to be retrieved, the network is trained to retrieve a map of the spatial gradient of the unknown contrast. Finally, such an augmented shape is turned into a map of the unknown permittivity by means of a simple post-processing. The framework is computationally effective, since all processing steps are performed in real-time. To provide an example of the achievable performance, Fresnel experimental data have been used as a validation. [ABSTRACT FROM AUTHOR]

Several researches have confirmed the possibility of localizing small anomalies via Kirchhoff migration (KM); however, when the background information is unknown, small anomalies cannot be satisfactorily retrieved. This fact can be examined through the simulation results; however, related theoretical result to explain the reason of such phenomenon has not yet been investigated. In this contribution, we show that the imaging function of the KM can be expressed by an infinite series of the Bessel function of the first kind, material properties, and antenna arrangement, and applied alternative value of the background wavenumber. Based on the theoretical result, we explain why the exact location and shape of anomalies cannot be retrieved. The simulation results with synthetic data exhibited to support the theoretical result. [ABSTRACT FROM AUTHOR]

Materials with characteristics of omnidirectional reflectionless absorption are of great importance in electromagnetic shielding and anechoic chamber. Recently emerged absorbing surfaces mostly work at a given angle, and absorbers working at a broad frequency band usually suffer from a 3-D structure. Thus, the fabrication of the ultra-thin and omnidirectional reflectionless absorber working at dual-frequency remains a technical challenge. In this work, we demonstrated the absorbing performance of an ultra-thin slab under different incident angles of the electric fields, and an approximate solution was derived to prove the realizability of the dual-frequency and omnidirectional reflectionless absorption. For illustration, a miniaturized dual-frequency anechoic chamber with a 343.8 mm diameter was constructed, and the full-wave simulations validated the results. The proposed design method is simple and robust, which provides a new clue for solving the practicability difficulty faced by existing anechoic chambers and microwave imaging systems. [ABSTRACT FROM AUTHOR]

In this study, a linear approach, linear sampling method (LSM) is used to reconstruct the shape of perfectly electric conductors (PEC) with the help of deep learning as a post-processing technique. In microwave imaging, the LSM is a simple and reliable linear inversion technique for determining the morphological features of unknown objects under investigation. However, the output of this method depends on the frequency of operation, the choice of regularization parameter,and it is unable to produce satisfactory results for objects with complex shapes. To overcome this drawback, a deep learning approach is used in this work, which can produce a better output in terms of accuracy, resolution. Here, the rough estimate of the PEC scatterer obtained using LSM is used to train the U-Net based convolutional neural network, which maps this output with the corresponding ground truth profiles. The proposed hybrid model is validated using several examples of synthetic and experimental data. [ABSTRACT FROM AUTHOR]

The application scenarios of near-field focusing metasurfaces usually require scanning the target area. Passive metasurface requires a turntable to complete scanning due to its limited functionality. The active metasurface typically has a high cost because it needs to load PIN diodes. To address this issue, the article introduces a 1-bit reconfigurable metasurface that can achieve multi-focus tunability under fixed polarization through a rotating array. The 1-bit polarization-independent metasurface unit consists of three layers of metal. The top layer of the unit consists of three rectangular patches in the X-direction; the middle layer is a cross-shaped patch structure; and the bottom layer is a metal ground. The cross-shaped structure in the middle layer can easily provide the 1-bit reflection phase required for two orthogonal polarizations independently. Using a vertically polarized horn to illuminate the metasurface, the top layer's X-direction rectangular patches do not provide phase for vertical polarization. By rotating the array where the cross-shaped patches are located by 90°, the phase shift provided can achieve two focal points. On this basis, rotate the upper array by 90°, making the rectangular patches change from theX-direction to the Y -direction. Meanwhile, the current of the cross-shaped patches is blocked under vertical polarization illumination. By changing the upper rectangular patches, a third independent phase can be provided. After size optimization, a third focus can be formed. The proposed 1-bit focusing-adjustable metasurface array has a simple structure, low cost, and enhanced utilization rate of the metasurface array. It has a high application prospect in projects such as microwave imaging. [ABSTRACT FROM AUTHOR]

This work presents the design and experimental validation of a compact frequency reconfigurable coplanar waveguide (CPW)-fed ultra-wideband (UWB) antenna with a capability to on-demand reject WLAN frequencies within the range of 5.15 GHz to 5.85 GHz, specifically tailored for applications in microwave imaging. The design and experimental results are presented and discussed. The bandwidth enhancement is obtained by using T-shaped slots between the feedline and the ground plane of the antenna, and the WLAN band is rejected by using an open loop resonator (OLR) placed on the antenna backside. Switching between UWB with and without WLAN band-notched modes is performed using a PIN diode and a bias circuit. The simulation results corroborate well with the experimental data and clearly showed an interesting frequency reconfigurable behavior for use in microwave imaging applications. An antenna performance simulation and analysis model is presented for breast tumor detection, and the tumor effect has been noticed for both operating modes of the antenna.

Non-melanoma skin cancer (NMSC) is among the most prevalent forms of cancer originating in the top layer of the skin, with basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) being its primary categories. While both types are highly treatable, the success of treatment hinges on early diagnosis. Early-stage NMSC detection can be achieved through clinical examination, typically involving visual inspection. An alternative, albeit invasive, method is a skin biopsy. Microwave imaging has gained prominence for non-invasive early detection of various cancers, leveraging distinct dielectric properties of healthy and malignant tissues to discriminate tumors and categorize them as benign or malignant. Recent studies demonstrate the potential of terahertz (THz) spectroscopy for detecting biomarkers by aligning electromagnetic wave frequencies in the low THz range (0.1 to 10 THz) with resonant frequencies of biomolecules, such as proteins. This study proposes an innovative microscale biosensor designed to operate in the THz range for the high-sensitivity and efficient diagnosis of non-melanoma skin cancer. By incorporating meticulously designed metamaterial layers, the sensor's absorption properties can be controlled, a critical aspect for discriminating between normal and NMSC-affected skin. In particular, the interaction between skin and THz waves, influenced by dielectric properties and unique vibrational resonances of molecules within tissue, is crucial for wave propagation and scattering. Extensive numerical studies showcased the suitability of the proposed biosensor for NMSC diagnosis, illustrated through specific case studies. These findings hold the potential to pave the way for further development of non-invasive microwave-imaging-based techniques for detecting NMSC and other types of skin cancer.

In this paper, a novel, cost-efficient 2-by-32 switch matrix for microwave imaging systems is designed and implemented. The switch’s operation was verified, with its performance assessed across different ports through testing and measurements. The microwave switch demonstrated versatility as it is able to handle up to 32 antennas, functioning as both transmitters and receivers. The switch is designed as part of a microwave imaging system for biomedical applications operating at 1 GHz. Nevertheless, the switch’s operability was beyond the required 1 GHz range, suggesting its potential use for a broad range of applications. Further, the microwave switch operation is automated, enabling computerized control with different modes of operation. The compact and portable design, automated control, and compatibility features make the microwave switch matrix a valuable component for microwave imaging applications.

This work aims to simplify the characterization process of coded-apertures for computational imaging (CI) at microwave frequencies. A major benefit of the presented technique is the minimization of the processing time needed to calculate the system sensing matrix for microwave CI-based compressive sensing applications. To achieve this, a deep learning-based approach which is capable of generating the sensing matrix using features learned directly from the coded-aperture distribution is proposed. To avoid the vanishing gradient problem, the proposed deep learning network contains skip connections. Using a dataset of 1,000 testing samples, the average normalized mean-squared-error (NMSE) calculated between the sensing matrix generated by the conventional method and that predicted by the proposed network is 0.0036. Moreover, the average mean-squared-error (MSE) calculated between the images reconstructed using the conventional and the predicted sensing matrix is 0.00297. In addition to providing high-fidelity estimations with minimized error, we demonstrate that using the trained network, the prediction of the sensing matrix can be achieved in 0.212 s, corresponding to a 65% reduction in the computation time needed to calculate the sensing matrix. This has significant outcomes in achieving real-time operation of CI-based microwave imaging systems.

A series of advanced equipment exposed to sub-/supercritical water environments at high temperatures, high pressures, and extreme water chemistry with high salt and dissolved oxygen content faces serious corrosion problems. Obtaining on-site corrosion data for typical materials in harsh environments is crucial for operating and maintaining related equipment and optimizing various corrosion prediction models. First, this article introduces the advantages and disadvantages, usage scenarios, and future development potential of several in situ monitoring technologies, including ultrasonic thickness measurement, the infrared thermography method, microwave imaging, eddy current detection, and acoustic emission. Considering the importance of electrochemical corrosion data in revealing microscale and nanoscale corrosion mechanisms, in situ testing techniques such as electrical resistance probes, electrochemical corrosion potential, electrochemical impedance spectroscopy, and electrochemical noise that can be applied to sub-/supercritical water systems were systematically discussed. The testing platform and typical data obtained were discussed with thick and heavy colors to establish a mechanical prediction model for corrosion behavior. It is of great significance to promote the development of corrosion monitoring techniques, such as breaking through testing temperature limitations and broadening the industrial application scenarios and maturity. [ABSTRACT FROM AUTHOR]

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China's first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar. [ABSTRACT FROM AUTHOR]

Microwave imaging technique allows obtaining images of hidden objects in structures and media using microwaves. Usually in short-range microwave imaging systems, the back-scattered signal is used, when a combined transmit-receive antenna scans over a plane, forming a two-dimensional synthesized aperture, while the signal reflected from the object of observation is recorded, as a result of which a microwave hologram of the object is formed. The second option involves registering the forward-scattered signal, when the transmitting and receiving antennas are located on opposite sides of the object and scan synchronously. The purpose of this work is a theoretical and experimental comparison of these two sounding options, identifying the advantages and disadvantages of each option, taking into account the features that arise when solving various problems of microwave imaging. [ABSTRACT FROM AUTHOR]

Microwave imaging has been widely used in stroke diagnosis as a non‐invasive, ionizing radiation‐free imaging method. However, the electromagnetic inverse scattering problems of microwave imaging are nonlinear and ill‐posed. To further improve the accuracy of microwave imaging algorithms to identify and reconstruct stroke regions. A novel Encoder‐Decoder and Modified U‐Net (ED‐MUNET) network for microwave imaging of stroke is proposed. The newly proposed ED‐MUNET method accomplishes the initial imaging from scattered data to stroke images through an encoder–decoder in the first step. In the second step, the feature information of the reconstructed image is extracted through the modified U‐Net network, and the reconstruction from the rough stroke image to the high‐resolution image is realized. The second step avoids black‐box operations and improves image accuracy. ED‐MUNET has fewer artifacts, the relative reconstruction error is less than 0.05, and the reconstructed images are clearer, according to the comparative experiments with other networks. The experimental results showed the superiority of the proposed method for reconstructing stroke images. [ABSTRACT FROM AUTHOR]

Objectives Determine the proportion of breast cancers for which MARIA® findings correspond to the cancer, with stratification by breast density and histological type. Investigate performance in symptomatic lesions. Gain patient feedback on experience with MARIA®. Methods MARIA® uses a radio frequency antennae array to measure signal attenuation and back scatter to build up a 3D map of tissue dielectric values. The study was a prospective, single-centre, interventional, post-approval device study. Recruitment Patients were eligible if they were attending symptomatic breast clinic or had confirmed or suspected breast cancer from any referral source. Recruitment between May 2018 and March 2020. Reading Regions of higher signal compared to background or distinct by shape were considered candidates for lesion correspondence. Up to 4 candidate regions per breast were annotated in likelihood order for representing a true lesion. Results 389 patients were recruited, and 6 excluded. 114 patients recruited with breast cancers (2 bilateral, 5 multicentric). 57 (47%) malignant lesions showed correspondence between the MARIA® signal and the cancer. Higher correspondence was seen in invasive (50%) than in situ (29%) disease. There was no reduction in correspondence at higher breast density. Reduced signal correspondence in the central scan volume and for small lesions. MARIA® scanning was well tolerated. Conclusions We believe that MARIA® signal corresponds to a malignant lesion in 47% of breast cancers examined. Advances in knowledge Our study, the largest to date for this type of technology, demonstrates successes and limitations of this MARIA® M6 version. [ABSTRACT FROM AUTHOR]

To assess the performance of microwave brain imaging (MWBI) systems, an anthropomorphic multifunctional head phantom with long life, low cost, and reliable dielectric properties over the intended frequency range is a principal requisite. A solid head model has been developed using a mold casting method, incorporating graphite, aluminum oxide, carbon black powder, and brass powder with epoxy/hardener. Four healthy solid brain tissues consisting of skin, skull, gray matter, and white matter are fabricated to mimic realistic head tissue properties along with cerebrospinal fluid. The constructed head model incorporates four cavities of varying sizes and positions, designed to accommodate healthy or unhealthy cores, thereby simulating diverse stroke scenarios without necessitating separate healthy and unhealthy models. Utilizing a single‐head model preserves dielectric properties during comparative analysis (healthy/unhealthy), except for core components. Tissue properties were measured twice, three months apart, showing good agreement between theoretical and measured results. [ABSTRACT FROM AUTHOR]

This study explores the potential of implementing the focusing properties of a virtual ideal Veselago lens within a standard free-space microwave imaging scenario. To achieve this, the virtual lens is introduced as an inhomogeneous numerical background for the inverse source problem. This numerical Vesealgo lens is incorporated into the incident and scattered field decomposition, resulting in a new data equation that involves the Veselago lens Green's function. In addition to the contrast sources within the object-of-interest, the lens introduces virtual contrast sources along the lens boundaries that depend on the total tangential magnetic field. It is shown that a surface integral contribution that takes into account these surface contrast sources must be added to the collected free-space data before one can invert using the well-conditioned Veselago lens inversion operator. A preliminary investigation of the accuracy to which this surface integral contribution must be computed is performed using additive Gaussian noise. Results show that an error of less than one percent is required to achieve imaging performance similar to utilizing an actual Veselago lens. All results are performed within a 2D simulation environment. [ABSTRACT FROM AUTHOR]