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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]

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 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]

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]

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]

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]

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]

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]

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]

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]

Microwave nondestructive testing (NDT) techniques show promise for composite inspection due to microwave signals' ability to penetrate and interact with internal structures. However, current microwave imaging approaches have poor spatial resolution, struggling to distinguish defects from defect-free regions. This limits reliable subsurface analysis and widespread adoption. This paper introduces a novel multi-frequency microwave imaging fusion method using an open-ended waveguide and deep learning to enhance defect detection accuracy in carbon fiber-reinforced polymer (CFRP) composites. The proposed technique employs an optimized feature extraction strategy to improve differentiation between defective and sound areas for superior subsurface visualization. The method leverages VGG-19 for efficient feature extraction and parallel processing to merge information from multi-frequency images. Our method significantly improved detection accuracy and F1 score, surpassing non-deep learning image fusion techniques by at least 50%. The optimized fusion strategy enables clear visualization of various defect types across multiple subsurface layers. Our approach also reduces computation time versus standard VGG implementation by 3–4×, showing scalability. The results demonstrate the proposed method's potential to overcome existing constraints and provide rapid, accurate subsurface analysis of complex CFRP structures through an optimized deep learning framework, holding significance for expanding microwave NDE applications. [ABSTRACT FROM AUTHOR]

A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors. [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.

Microwave imaging has become an active research area in recent years, owing primarily to advancements in detecting the early stages of cancer. The study aimed to create a high-gain compact Vivaldi Tapered Slot antenna (VTSA) for microwave imaging in medical applications and also aims to address several challenges in the development of microwave imaging (MWI) technology for medical applications. These challenges include the ability to detect and identify abnormalities in human tissue and considering safe Specific Absorption Rate (SAR) limits for patients, the approach of balancing of penetration and resolution can be done on the design. The antenna operates at frequencies ranging from 1.7 to 3.1 GHz and is built on a low-cost Flame Retardant-4 (FR-4) substrate with a thickness of 1.6 mm. A compact exponential VTSA is initially presented while designing the proposed antenna for broad impedance bandwidth performances. The simulation used a back-to-back linear array of antennas with or without a phantom, specifically a without phantom (only antennas), a water phantom (cube shape), and an anomaly inside the water phantom. The results revealed a significant shift in the signal graph between the three results, indicating a difference in values between the three simulations. A transient domain solver calculation was used in the simulation. The designed antenna improved a gain of 6.09 dBi and a SAR of 0.326 W/kg by maximizing the edges of the exponential in the tapered section and the feedline slot area. The antenna exhibits differences in scattering parameters on each simulation of anomalies across the required frequency range. The result finds suitability of the experiment and simulation in assessing the microwave imaging capabilities. With the data presented, simulated antennas can be used for microwave imaging. The next study should aim on making a suitable imaging system with dimensions that supported in the antenna range and specifications.

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]

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.

Water, the source of life, is a more abundant, low‐cost, and environment‐friendly candidate material for tunable meta‐devices compared to conventional approaches in the microwave region. Wavefront shaping can be flexibly manipulated by adjusting the shapes and temperature of water‐based meta‐atoms, but individual control of each meta‐atom is complicated and inconvenient, especially for continuous tunability. Here, we propose a design strategy based on the phase compensation mechanism to continuously tune the properties of water‐based meta‐lenses at 5 GHz. Integrated‐resonant units (IRUs) with coupled water pillars are carefully designed to effectively tailor the balance between polarization conversion efficiency and phase compensation. By changing the filling height of water in the designed meta‐lens from 10 to 60 mm, the focal length can be tuned from 228 to 424 mm. The off‐axis focusing of the meta‐lens is also demonstrated in which the deflection angle is adjustable from around 0° to 5°. This work will pave the way for designing tunable water‐based meta‐devices and facilitating their applications in microwave imaging systems. [ABSTRACT FROM AUTHOR]

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications. [ABSTRACT FROM AUTHOR]

Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems. [ABSTRACT FROM AUTHOR]

Microwave and antenna systems have the potential to revolutionize healthcare by offering innovative solutions for medical diagnosis, treatment, and patient monitoring. Researchers in various fields are collaborating with medical practitioners to develop next-generation healthcare technologies. Examples include using microwave sensors for stroke detection in ambulances, microwave radiation for breast screening, and microwave-based techniques for cancer treatment. The recent advancements in artificial intelligence and machine learning are also accelerating the development of microwave-based techniques. This special issue presents a comprehensive exploration of microwave and antenna systems in medical applications, featuring original research articles and reviews that highlight their transformative impact on healthcare. [Extracted from the article]

Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of image processing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests. [ABSTRACT FROM AUTHOR]

The letter presents a method of frequency to time domain conversion of Scattering Parameter data for microwave imaging applications. The time to frequency domain conversion was initially tested in the MATLAB. The testing was conducted in a realistic microwave imaging environment with an antenna and a heterogeneous breast phantom. Note that, the time domain signal obtained from the Vector Network Analyzer (VNA) as well as the time domain signal obtained with the introduced process is similar indicating efficiency of the proposed method. An ultra‐wideband (UWB) signal is transmitted from the transmitting Antipodal Vivaldi Antenna to the breast phantom and the received signals are collected by the same antenna over a monostatic process. A total of 36 antenna rotations were considered while scanning the phantom from various orientations. All the data stored in the VNA is extracted and the proposed frequency to time domain conversion technique was applied to it. Finally, the Delay and Sum beamforming algoirithm was used to reconstruct the target image. [ABSTRACT FROM AUTHOR]

Antennas are significant passive components in Microwave Imaging (MWI) system. The proposed work focuses on the design and analysis of a Vivaldi antenna of size 45 × 40 × 1.6 mm3 for breast cancer diagnosis. The proposed antenna utilizes an FR4 substrate and offers a wideband response. The suggested antenna design is based on a tapered slot antenna. The design utilizes microstrip slot line transition feed as it provides good impedance matching and wide bandwidth. The proposed antenna's design attributes like the radius of the slot and tapering rate are optimized through parametric analysis to achieve desired ultra-wideband (UWB) performance. The UWB offered by the designed antenna is 13.87 GHz (2.79 GHz-16.66 GHz). A Voltage Standing Wave Ratio (VSWR) of less than 2 is obtained for the entire resonating frequency range. The proposed antenna exhibits 60% size reduction compared to the conventional Vivaldi antenna with a peak gain and directivity of 4.77 dBi and 5.84 dBi, respectively. A breast phantom has been designed and simulated for Specific Absorption Rate (SAR) calculation. The designed structure exhibits an average SAR of 0.997 W/kg. Further, the proposed antenna is fabricated and tested. The measured results agree with simulation findings. Hence, the compactness and radiation performance of the proposed antenna makes it suitable for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Internationally, the relentless toll of breast cancer on women's lives persists, emphasizing the critical need for advancements in early detection methods. This paper delves into the promising domain of microwave imaging technology, where antennas play a pivotal role. It explores the performance of a single Antipodal Vivaldi Antenna (AVA) positioned at various angles around a breast phantom, with the paramount goal of revolutionizing breast cancer detection by meticulously comparing the antenna's Reflection loss, Gain, and H-field characteristics at 0°, 30°, and 60° angles. Considering the antenna's intended use in bioapplications, the paper also evaluates its Specific Absorption Rate (SAR) performance. Simulations are conducted using the designed antenna and a breast phantom model featuring four layers of tissue, and the results are scrutinized against established safety standards for bio applications [ABSTRACT FROM AUTHOR]

Reconfigurable metasurfaces exhibit the capability of flexibly controlling electromagnetic (EM) waves, and the real‐time manipulation of tailored EM wavefront in full‐space is one of the steadily increasing interests. This paper proposes a reconfigurable transmission‐reflection‐integrated (TRI) coding metasurface, combining the functionalities of reconfigurable transmission and reflection metasurfaces. The meta‐particle is composed of a multilayer structure integrated with three positive‐intrinsic‐negative (PIN) diodes. By dynamically controlling the states of these diodes with the direct‐current (DC) bias voltage from a field‐programmable‐gate‐array (FPGA), meta‐particles achieve switching between transmission mode and reflection mode and independently modulate phase response. Metasurfaces can manipulate the reflected and transmitted wavefront by array encoding, allowing for full‐space control of the EM wavefront. Additionally, this paper demonstrates various functions implemented on this metasurface, including mode switching, beam scanning, shaping, focusing, and spatial scattering. As a proof of concept, a metasurface array prototype is fabricated and experimented. The simulation and measurement results agree well, confirming the effective ability of reconfigurable TRI metasurfaces to manipulate EM waves throughout full‐space. The proposed reconfigurable TRI coding metasurface opens the door for flexible wavefront control in the full‐space domain, with broad application prospects in areas such as microwave imaging, EM stealth, and smart radomes. [ABSTRACT FROM AUTHOR]

Summary: A compact and novel ultrawideband (UWB) antenna integrated with Ku band for microwave imaging applications is reported. The reported antenna consists of a novel patch structure along with a tapered feed structure on top of FR‐4 substrate having a compact dimension of 16 × 22 mm2 and novel semicircular defected ground structure (SC‐DGS) etched below the substrate. The proposed antenna covers a wide impedance bandwidth of 15.07 GHz spanning from 3.51 to 18.58 GHz with a fractional bandwidth (FBW) of 136%. The intended antenna performance is analyzed for S11, peak gain, efficiency, and radiation patterns in frequency domain, and group delay, isolation (S21), and linear phase response in time domain. The simulated and measured results of reported antenna are well matched for the usage in practical portable UWB applications. More importantly, the proposed antenna system is simulated for the detection of breast cancer tissue by comparison of S21 results, current densities, and specific absorption rate with and without tumor conditions. These findings show that the radiator and the whole system work well in detecting the breast tumor. [ABSTRACT FROM AUTHOR]

The field of microwave and millimeter-wave imaging systems is experiencing significant growth in research, primarily attributed to their versatile interaction capabilities with various materials. In the course of image reconstruction, the inverse scattering problem is resolved through either qualitative or quantitative means. The inverse scattering problem can be resolved by utilizing the truncated singular value decomposition (TSVD) algorithm, which is a suitable inversion method that can be employed when there is a need to discard a portion of data. The utilization of multi-frequency multi-static inversion techniques yields results that exhibit greater robustness and clarity when contrasted with single-frequency mono/bi-static configurations. However, it should be noted that the TSVD formulation may not be sufficient in practical experimental situations, as it fails to account for potential deviations in power among excitations at varying frequencies. As a result, it is imperative to perform a calibration process to standardize the power variations across different frequencies. The proposal outlines a calibration methodology that aims to achieve power equilibrium across various frequencies and facilitate the automation of imaging algorithms. The proposed calibration technique is evaluated and demonstrated through the use of a through-the-wall experiment. [ABSTRACT FROM AUTHOR]

Microwave imaging (MI) is a noninvasive and nonionizing procedure for detection of cancerous cells in healthy body tissues using radiofrequency (RF) and microwaves. The procedure involves the use of Ultra Wide Band (UWB) antennas for sensing purposes. Therefore, this research article presents the design, development, and testing of a low‐profile UWB Archimedean spiral microstrip‐patch antenna (ASMA) for detection of skin cancer using monostatic radar‐based microwave imaging. The proposed ASMA consists of a spiral resonator with a defective ground structure and a slotted microstrip feed line with dimensions of 38 × 38 × 0.87 mm3. The proposed antenna shows an impedance bandwidth for the frequency range of 2.2–13.9 GHz, with a peak gain of 6.8 dB at 7.8 GHz. In silico analysis of the proposed ASMA for MI is carried out with Gaustav model using Computer Simulation Technology Microwave Studio. To validate the performance of the ASMA as a sensor for MI, a prototype of the same is fabricated and a four‐layered bio‐phantom of the human forearm is prepared for in vitro and in vivo testing of the proposed procedure. The validation of ASMA radiation properties is done using a Vector Network Analyser (E‐5063A) (VNA) and an anechoic chamber with the fabricated antenna at 10 mm away from the prepared bio‐phantom. The recorded S parameter data with bio‐phantom and the VNA is processed using different beamforming algorithms like Delay and Sum and Coherent Factor‐Delay Multiply and Sum (CF‐DMAS) to reconstruct the image of the scanned area. The reconstructed images are 97%–98% accurate. The proposed ASMA sensor is also safe for human exposure as it has a specific absorption rate of 0.0546 W/Kg at 5 GHz that complies with the safety guidelines of the Federal Communications Commission to minimize potential health risks associated with exposure to RF and microwave radiation. [ABSTRACT FROM AUTHOR]

The distorted Born iterative method (DBIM) is a popular quantitative reconstruction algorithm for solving electromagnetic inverse scattering problems. These problems are non-linear and ill-posed. As a result, the efficiency of the method is limited by local minima. To overcome this, a correct initial guess solution is needed to obtain a satisfactory result. The U-Net based Convolutional Neural Network (CNN) is used in this study to make a good initial guess for the DBIM technique. The permittivity estimate produced at the output of U-Net is then refined using an existing iterative optimization process. This method's findings are compared with the conventional DBIM approach. Strong scattering profiles of synthetic and experimental datasets with homogeneous and heterogeneous scatterers are investigated to validate the efficiency of the proposed technique. The results suggest that the use of the deep learning technique for an initial guess of DBIM improves accuracy and convergence rate significantly. [ABSTRACT FROM AUTHOR]

A combinatorial quantitative reconstruction method employing Subspace-based Virtual Born Iteration Method (SVBIM) along with a greedy compressive sensing algorithm, Sparse Reconstruction by Separable Approximation (SpaRSA) to solve the ill-posed inverse problem in microwave imaging is proposed in this paper. SVBIM makes use of the contribution of the variational induced current to arrive at a better estimate of the permittivity profile in each iteration. SpaRSA operates in the sparse domain and reduces the computational overload, thereby guiding the inverse problem towards a faster global optimum solution. The merger of these two algorithms helps to reconstruct breast profiles having high-permittivity tumor inclusions (e = 60) with reduced error. The proposed reconstruction method is capable of extracting the salient information regarding tissue differentiation (permittivity and conductivity) and dielectric distribution of various tumor and fibroglandular inclusions, dimensions, resolution, size, shape, and coordinate localization of inclusions. In comparison to various methods reported in literature, the results obtained using the proposed method are highly encouraging. In the presence of 30 dB noise, the above-said imaging technique produces a significantly reduced permittivity error value of 0.47 in the reconstruction of tumor inclusions as against 0.85 and 0.71 in the case of TV norm and Re-weighted Basis Pursuit methods, respectively. The experimental validation is carried out using a phantom having three inclusions of sizes 10 mm, 6 mm, and 3 mm. The inclusions have been localized successfully with errors of 0.089, 0.133, and 0.21, respectively. [ABSTRACT FROM AUTHOR]

This paper presents microwave head imaging for the detection of tumours and experimental verification using a semi-solid complex multilayer head phantom. Phantoms are an inevitable part of medical diagnosis for obtaining an optimum design before trials in humans. Simple chemical compositions are used to create a material with electrical characteristics that are identical to those of sensitive human brain tissues. The artificially fabricated soft tissues are housed in a man-made substance that has a human skull-like form. This paper describes advancements in the development and measurement of a head phantom, devoted to the testing of a microwave-based head imaging in a realistic laboratory-controlled configuration for brain tumour detection. Dielectric property measurements are performed using a vector network analyser (VNA) with a dielectric probe arrangement and an antipodal Vivaldi antenna (48 mm × 21 mm) with a resonance frequency of 4 GHz. A total of 201 measurements were taken at room temperature (25°C), and a frequency range of 1–6 GHz was used for electrical characterization of the phantom. Measurements were taken multiple times per day, and the average value of the dielectric properties of the phantom components mimicked actual head tissues. The measured reflection coefficient (S11) value with the tumour was −28 dB and that without the tumour was −33 dB for a tumour radius of 10 mm. These measured parameters were used to construct an image in MATLAB using different confocal imaging algorithms. The experimental results for the final phantom product emulated the properties of the required tissues and the location of the tumour. [ABSTRACT FROM AUTHOR]

This article deals with the study, design, fabrication, and measurement of a ultra-thin ultra-wideband (UWB) microstrip antenna. As the proposed antenna design is compact, ultra-thin, and optimized in size, it can be used for microwave imaging and wearable applications. Innovation has been used to design the UWB microstrip antenna, which has increased the efficiency and performance of the antenna. This innovation has considered the feed structure, geometry, modifications in the slots, and the use of an ultra-thin dielectric substrate with a thickness of 0.25 mm. The proposed technique is a unique tuning mechanism located under the feed line of the proposed UWB microstrip antenna. The antenna's compact and ultra-thin design is desirable for reducing the complexity of the physical array structure and achieving a degree of body fit. Parametric studies are performed to compare this performance after changes in patch and ground plane length. Finally, the simulated performance parameters were analyzed and compared with the measured results. Due to its small size, new shape, and impressive functions, the proposed antenna is well suited to multifunctional healthcare applications and sports performance monitoring. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for robust reconstruction results. This paper proposes a neural network called RootsNet, which integrates the CS mechanism into the network to prevent error propagation. So, RootsNet knows what will happen if some modules in the network go wrong. It also implements real-time and successfully reconstructed extremely low measurement rates that are impossible for traditional optimization-theory-based methods. For qualitative validation, RootsNet is implemented in two real-world measurement applications, i.e., a near-field microwave imaging system and a pipeline inspection system, where RootsNet easily saves 60% more measurement time and 95% more data compared with the state-of-the-art optimization-theory-based reconstruction methods. Without losing generality, comprehensive experiments are performed on general datasets, including evaluating the key components in RootsNet, the reconstruction uncertainty, quality, and efficiency. RootsNet has the best uncertainty performance and efficiency, and achieves the best reconstruction quality under super low-measurement rates. [ABSTRACT FROM AUTHOR]

DEXTER (detection of explosives and firearms to counter terrorism) is a project funded by NATO's Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. While body scanner systems in secure areas of public places are becoming more and more efficient, the attack at Brussels airport on 22 March 2016, upstream of these systems, in the middle of the crowd of passengers, demonstrated the lack of discreet and real-time security against threats of mass terrorism. The NATO-SPS international and multi-year DEXTER project aims to provide new technical and strategic solutions to fill this gap. This project is based on multi-sensor coordination and fusion, from hyperspectral remote laser to smart glasses, artificial algorithms, and suspect identification and tracking. One of these sensors is dedicated to threat detection (large weapon or explosive belt) using the clothing of pedestrians by means of an active microwave component. This project is referred to as MIC (Microwave Imaging Curtain), also supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real-time to address non-checkpoint detection of explosives and firearms. The project, led by ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra-Wide Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC's innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar images using a deep-learning network. This paper will outline the project's objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results obtained during a live demonstration in a relevant environment. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has played a critical role in the enhancement and automation of global food production and delivery. The assessment of ripeness through microwave images and AI presents a unique opportunity to enhance the current process and minimize food wastage. Particularly in avocado maturity prediction, microwave imaging provides a non-invasive solution to see through the shell and predict the maturity intelligently. However, conventional deep learning models are generally data-hungry, requiring huge amounts of training data to maintain high performance. In this paper, we propose a few-shot learning (FSL) model to address the need for a large training dataset in maturity estimation. We classify avocado microwave images for maturity assessment through a prototypical FSL model. The FSL model achieves an accuracy range of 80%–96 % across the different experimental groups and all outperforms the conventional deep learning in scenarios where limited data is available for training. This experiment demonstrates the feasibility and accuracy of utilizing microwave scanning and FSL to determine avocado maturity.

For a successful application of subspace migration algorithm to retrieve the exact location and shape of small anomaly in microwave imaging, one must begin the reconstruction process under the assumption that complete information about the homogeneous background medium, such as background permittivity and conductivity, is available. In many studies, the statistical value of the background medium was adopted, raising the possibility of an incorrect value being applied. Thus, simulation results have been examined in order to identify cases in which an inaccurate location and shape of anomaly were retrieved. However, the theory explaining this phenomenon has not been investigated. In this paper, we apply an alternative wavenumber instead of the true one and identify the mathematical structure of the subspace migration imaging function for retrieving two-dimensional small anomaly by establishing a relationship with an infinite series of Bessel functions of the first kind. The revealed structure explains the reason behind the retrieval of an inaccurate location and shape of anomaly. The simulation results with synthetic data are presented to support the theoretical result.

Numerous studies have been made to design absorption–diffusion–integrated metasurface (ADIM) for the application of electromagnetic stealth. However, it is still a challenge to achieve an outstanding stealth effect and a simple structure simultaneously. In this article, we propose two simple hybrid copper–graphene units with similar absorptivity and a reflected phase difference of pi, which are utilized to construct a novel ADIM by using a simulated annealing algorithm with a scattering pattern prediction module. A sample with an overall size of 200 × 200 mm2 is designed, fabricated, and measured. Both simulation and experimental results are in good agreement, which fully demonstrate the design method. The proposed ADIM with only a single functional layer exhibits more than 20 dB radar cross section reduction from 7.5 to 13.5 GHz maintained to 20° oblique incidence, which could address this challenge successfully. Power loss density and surface current distributions of the proposed two units are given to provide a physical insight of the mechanisms. The proposed ADIM can enable a wide application in stealth cloak, microwave imaging, wave front manipulation, and so on. [ABSTRACT FROM AUTHOR]

Inspection of thick and low-density multilayer composite structures is of paramount importance. X-ray computed tomography (CT) and phased array ultrasonic testing (PAUT) are widely employed modalities for non-destructive testing (NDT) of these composites. Owing to low density of constituent materials typically utilised to construct composites and rich wave scattering within their structures, inspecting them using X-ray CT and PAUT does not always yield acceptable flaw detection results. On the other hand, microwave NDT techniques have shown to be particularly suitable for inspecting thick multilayer non-carbon-based composites. However, the relative performance of emerging microwave NDT techniques and the widely accepted X-ray CT and PAUT is yet to be established. This paper provides first-of-its-kind experimental comparison between microwave, X-ray CT, and PAUT on a comprehensive set of thick composite samples with different defect types. It is demonstrated herein that microwave NDT performed on par with X-ray CT in terms of defect detection capability (qualitatively and quantitatively), and in many cases outperformed PAUT. A detailed summary overviewing the performance, advantages and shortcomings of each method for particualr defect types is included which disseminates new knowledge to benefit practitioners and researchers alike. [ABSTRACT FROM AUTHOR]

Microwave magnetometry is essential for the advancement of microwave technologies. We demonstrate a broadband microwave sensing protocol using the AC Zeeman effect with ensemble nitrogen-vacancy (NV) centers in diamond. A widefield microscope can visualize the frequency characteristics of the microwave resonator and the spatial distribution of off-resonant microwave amplitude. Furthermore, by combining this method with dynamical decoupling, we achieve the microwave amplitude sensitivity of 5.2 μ T / Hz , which is 7.7 times better than 40.2 μ T / Hz obtained using the protocol in previous research over a sensing volume of 2.77 μ m × 2.77 μ m × 30 nm. Our achievement is a concrete step in adapting ensemble NV centers for wideband and widefield microwave imaging. [ABSTRACT FROM AUTHOR]

Studies have indicated that the decrease in the extent of Arctic sea ice in recent years has had a significant impact on the Arctic ecosystem and global climate. In order to understand the evolution of sea ice, it is becoming increasingly imperative to have continuous observations of Arctic-wide sea ice with high spatial resolution. Passive microwave sensors have the benefit of being less susceptible to weather, wider coverage, and higher temporal resolution. However, it is challenging to retrieve accurate parameters of sea ice due to the low spatial resolution of passive microwave images. Therefore, improving the spatial resolution of passive microwave images is beneficial for reducing the uncertainty of sea ice parameters. In this paper, four competitive multi-image super-resolution (MISR) networks are selected to explore the applicability of the networks on multi-frequency Advanced Microwave Scanning Radiometer 2 (AMSR2) images of Arctic sea ice. The upsampling factor is set to 4 in the experiment. Firstly, the optimal input lengths of the image sequence for the four MISR networks are found, and then the best network on different frequency band images is further identified. Furthermore, some factors, including seasons, sea ice motion, and polarization mode of images, that may affect the super-resolution (SR) results are analyzed. The experimental results indicate that utilizing images from winter yields superior SR results. Conversely, SR results are the worst during summer across all four MISR networks, exhibiting the largest difference in PSNR of 4.48 dB. Additionally, the SR performance is observed to be better for images with smaller magnitudes of sea ice motion compared to those with larger motions, with the maximum PSNR difference of 2.04 dB. Finally, the SR results for vertically polarized images surpass those for horizontally polarized images, showcasing an average advantage of 4.02 dB in PSNR and 0.0061 in SSIM. In summary, valuable suggestions for selecting MISR models for passive microwave images of Arctic sea ice at different frequency bands are offered in this paper. Additionally, the quantification of the various impact factors on SR performance is also discussed in this paper, which provides insights into optimizing MISR algorithms for passive microwave sea ice imagery. [ABSTRACT FROM AUTHOR]

Fourier-based imaging has been widely adopted for microwave imaging thanks to its efficiency in terms of computational complexity without compromising image resolution. Together with other backpropagation imaging algorithms like delay-and-sum (DAS), they are based on a far-field approach to the electromagnetic expression relating to fields and sources. To improve the accuracy of these techniques, this contribution presents a modified version of the well-known Fourier-based algorithm by taking into account the field radiated by the Tx/Rx antennas of the microwave imaging system. The impact on the imaged targets is discussed, providing a quantitative and qualitative analysis. The performance of the proposed method for subsampled microwave imaging scenarios is compared against other well-known aliasing mitigation methods. [ABSTRACT FROM AUTHOR]

Aiming at the problems of synthetic-aperture radar (SAR), such as high sampling rate and vulnerability to noise interference in imaging, a sparse reconstruction algorithm based on approximate observation and L1/2 threshold iteration is proposed in this paper. To solve the problem of the large dimension and high computational complexity of the measurement matrix, a sparse reconstruction model based on approximate observation is constructed, and a threshold iterative algorithm to rapidly solve the L1/2 regularization problem is introduced, which can realize sparse signal reconstruction with fewer sampling data and quickly solve the problem. The simulation results of point targets and the measured data of spaceborne SAR show that compared with the traditional matched filtering algorithm and L1 threshold algorithm based on a two-step iteration, the proposed sparse reconstruction algorithm has a faster iteration speed and improves the image quality. [ABSTRACT FROM AUTHOR]