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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]

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]

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]

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]

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]

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 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.

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.

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]

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]

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]

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]

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]

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]

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]

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]

Microwave imaging for object localization utilizes the differences in scattering parameter data measured with and without the object of interest. However, in the real world, it is often impossible to measure incident fields in the background medium without the object. This contribution presents a method to estimate scattered field data through simple statistical processing of the measured total field data and apply it to reconstruct localized images of small moving objects. The effectiveness of the proposed method was experimentally verified.

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]

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]

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]

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]

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]

In this paper, we present the microwave imaging of anisotropic objects by artificial intelligence technology. Since the biaxial anisotropic scatterers have different dielectric constant components in different transverse directions, the problems faced by transverse electronic (TE) polarization waves are more complex than those of transverse magnetic (TM) polarization waves. In other words, measured scattered field information can scarcely reconstruct microwave images due to the high nonlinearity characteristic of TE polarization. Therefore, we first use the dominant current scheme (DCS) and the back-propagation scheme (BPS) to compute the initial guess image. We then apply a trained convolution neural network (CNN) to regenerate the microwave image. Numerical results show that the CNN possesses a good generalization ability under limited training data, which could be favorable to deploy in image processing. Finally, we compare DCS and BPS reconstruction images for anisotropic objects by the CNN and prove that DCS is better than BPS. In brief, successfully reconstructing biaxial anisotropic objects with a CNN is the contribution of this proposal. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]