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Modified split-ring resonators (SRR) are used develop a high gain UWB antenna for microwave imaging applications. This paper analyzes two different ground designs for the proposed patch radiator with modified SRR; defected ground structure and the second one is rectangular slotted ground. Two SRRs are placed on the circular radiator patch element, each in the shape of alphabet letters ‘S’ and ‘V’. The results of the first design with DGS demonstrate that the reflection coefficient is well below −10 dB in the frequency range from 1.9 GHZ −11.9 GHz with the fractional bandwidth of 110%. Further modification in the resonant modes of UWB is obtained by introducing single rectangular slot on the ground plane. This helps in eliminating the lower band from 1.9 GHz to 6 GHz. This band rejection helps in alleviating the interferences caused by other unlicensed wireless applications lying in sub 6GHZ band.

Emerging dynamic metasurface antennas (DMAs) technology promises to achieve future wireless communications by reducing hardware costs, physical size and power consumption. Especially in the field of radar imaging, they can be used as an effective alternative platform for modern computational imaging; because they can simplify the physical hardware and increase the data acquisition rate. Fourier transform (FT)-based scene image reconstruction techniques are known as cost-effective computing solutions for the imaging system processing unit. However, due to the physical layer compression in DMAs and the fact that they do not produce uniform radiation patterns, the information provided by them is not compatible with Fourier-based techniques and cannot be applied directly. In this paper, we first introduce a 3D near-field bistatic imaging approach using two one-dimensional (1D) DMAs as a panel-to-panel model in a Mills Cross structure. Then, based on the introduced mathematical model, we derive a Fourier-based algorithm for the image reconstruction problem. The proposed algorithm consists of five main steps: pre-processing (to transfer data provided by the transmitter and receiver DMAs to a set of equivalent spatial measurements), applying FTs to the transferred signal, filtering in the Fourier domain, a simplified interpolation, and applying a 3D inverse FT to retrieve scene information. The results of numerical simulations confirm the satisfactory performance of the proposed approach.

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

Microwave imaging of breast cancer is considered and a new microwave imaging prototype including the imaging algorithm, the antenna array, and the measurement configuration is presented. The prototype aims to project the geometrical features of the anomalies inside the breast to a single-slice image at the coronal plane depending on the complex dielectric permittivity variation among the tissues to aid the diagnosis.The imaging prototype uses a solid cylindrical dielectric platform, where a total of 24 optimized Vivaldi antennas are embedded inside to form a uniform circular antenna array. The center of the platform is carved to create a hollow part for placement of the breast and the multistatic, microwave scattering parameters are collected with the antenna array around the hollow center. The dielectric platform further enhances the microwave impedance matching against the breast fat tissue and preserves the vertical polarization during the measurements. In the imaging phase, a computationally efficient inverse electromagnetic scattering method-reverse time migration (RTM)-is considered and adapted in terms of scattering parameters to comply with the actual measurements.The prototype system is experimentally tested against tissue-mimicking breast phantoms with realistic dielectric permittivity profiles. The reconstructed single-slice images accurately determined the locations and the geometrical extents of the tumor phantoms. These experiments not only verified the microwave imaging prototype but also provided the first experimental results of the imaging algorithm.The presented prototype system implementing the RTM method is capable of reconstructing single-slice, nonanatomical images, where the hotspots correspond to the geometrical projections of the anomalies inside the breast.

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

International audience; In order to map the migration and introduction of farming into Europe during the 7th and 6th millennia Before Common Era, archaeologists have made a connection between the study of pottery and farming migration. We are interested here in the classification of pottery into coiling and spiral types, based on their manufacturing techniques. To distinguish between these two techniques, we look for the lines formed by air bubbles embedded in the pottery samples. Current methods make use of bulky systems, such as the Computerized Tomography scanners or synchrotrons. Microwave acquisition and processing offer an interesting alternative, due to the possibility to have compact and portable systems. In this paper, we investigate the classification of pottery based on Low-terahertz measurements in the D-band. We process the measurements with three-dimensional Fast Fourier Transform. The resulting matrix is classified with an Artificial Neural Network, the Multi-Layer Perceptron, which is optimized with the Grey Wolf Optimizer, a bio-inspired algorithm. The first results show that the accuracy reaches up to 99% using all the acquired spatial and frequency measurements. Then, we optimize the mm-Wave measurement system with a critical criterion on accuracy in two different scenarios. In the first scenario, we reduce the spatial acquisition but maintain the wideband operation and the results show that the accuracy is between 85% and 96%. In the second one, we reduce the spatial acquisition and use a single frequency. For this second scenario we achieve a classification accuracy which is between 77% and 100%.

In this paper, we are concerned with microwave subsurface imaging achieved by inverting the linearized scattering operator arising from the Born approximation. In particular, we consider the important question of reducing the required data to achieve imaging. This can help to reduce the radar system’s cost and complexity and mitigate the imaging algorithm’s computational burden and the needed storage resources. To cope with these issues, in the framework of a multi-monostatic/multi-frequency configuration, we introduce a new spatial sampling scheme, named the warping method, that allows for a significant reduction in spatial measurements compared to other literature approaches. The basic idea is to introduce some variable transformations that “warp” the measurement space so that the reconstruction point-spread function obtained by adjoint inversion is recast as a Fourier-like transformation, which provides insights into how to achieve the sampling. In our previous contributions, we focused on presenting and checking the theoretical background with simple numerical examples. In this contribution, we briefly review the key components of the warping method and present its experimental validation by considering a realistic subsurface scattering scenario for the case of a buried water pipe. Essentially, we show that the latter succeeds in reducing the number of data compared to other approaches in the literature, without significantly affecting the reconstruction results.

So far, numerous learned models have been pressed to use in microwave imaging problems. These models however, are oblivious to the imaging geometry. It has always been hard to bake the physical setup of the imaging array into the structure of the network, resulting in a data-intensive models that are not practical. This work put forward a graph formulation of the microwave imaging array. The architectures proposed is made cognizant of the physical setup, allowing it to incorporate the symmetries, resulting in a less data requirements. Graph convolution and attention mechanism is deployed to handle the cases of fully-connected graphs corresponding to multi-static arrays. The model works with a modular fashion at node level to strike a trade-off between flexibility and efficient capture of mutual information present in measured signals. Additionally, the modular working fashion endows the model with immunity to overfitting. The graph-treatment of the problem is evaluated on experimental setup in context of anomaly localization with imaging array and has shown higher performance as compared to the popular radar technique. The thin model was realized with a feasibly procured reasonably-sized dataset in the order of few hundreds, thus eliminating the need to resort to simulations for augmentation.

A defected ground antenna with dielectric reflector is designed and investigated for breast tumour diagnosis. Ultra-wide band resonance (3.1 to 10.6 GHz) is achieved by etching two slots and adding a narrow vertical strip in a patch antenna. A high dielectric constant substrate is added below the antenna, which shows remarkable effect on performance. Antenna performance is verified experimentally on an artificially fabricated breast tissue and tumour. Malignant tissue has different dielectric properties than the normal tissue, that causes deviation in the scattered antenna power. Average value of backscattered signal variation and ground penetrating radar (GPR) algorithm is used to localize the tumour of radius 4mm in breast tissue.

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

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

In this paper, we propose a fractional regularized distorted Born iterative method (DBIM) to solve non-linear ill-posed problems of microwave imaging. Fractional regularization is a modification to Tikhonov regularization, where singular values are weighed with fractional power. As a result, the well-known effect of oversmoothing present in Tikhonov regularization is reduced, thereby the output image quality is improved. The results of this method are compared with standard DBIM using Tikhonov regularization. Various numerical examples of simulated and experimental datasets containing homogeneous as well as heterogeneous scatterers are considered to validate the effectiveness of the proposed approach. It is found that the proposed method improves the accuracy of estimated images over conventional DBIM.

Worldwide, lung cancer is one of the most common causes of cancer-related death. Detected by computer tomography, it is usually removed through thoracoscopic surgery. During the surgery the lung collapses requiring some strategies to track or localize the new position of the lesion. This is particularly challenging in the case of minimally invasive surgeries when mechanical palpation is not possible. Here we undertake a preliminary study with numerical analysis of an ultra-wideband (UWB) radio technology which can be employed directly during thoracoscopic surgery to localize deep solitary pulmonary nodules. This study was conducted through Finite Difference Time Domain (FDTD) simulations, where a spherical target mimicking a nodule located between 1 and 6 cm of depth and an UWB pulse at several frequencies between 0.5 and 5 GHz was used for localization. This investigation quantifies the influence of several parameters, such frequency, lesion depth, and number of acquisitions, on the final confocal image used to locate a cancer in the lung tissue. We also provide extensive discussion on several artifacts that appear in the images. The results show that the cancer localization was possible at operational frequencies below 1 GHz and for deep nodules (>5 cm), while at lower depths and higher frequencies several artifacts hindered its detection., Bundesministerium für Bildung und Forschung

This work details an effective dynamic chemical etching technique to fabricate ultra-sharp tips for Scanning Near-Field Microwave Microscopy (SNMM). The protruded cylindrical part of the inner conductor in a commercial SMA (Sub Miniature A) coaxial connector is tapered by a dynamic chemical etching process using ferric chloride. The technique is optimized to fabricate ultra-sharp probe tips with controllable shapes and tapered down to have a radius of tip apex around ∼1 μm. The detailed optimization facilitated the fabrication of reproducible high-quality probes suitable for non-contact SNMM operation. A simple analytical model is also presented to better describe the dynamics of the tip formation. The near-field characteristics of the tips are evaluated by finite element method (FEM) based electromagnetic simulations and the performance of the probes has been validated experimentally by means of imaging a metal-dielectric sample using the in-house scanning near-field microwave microscopy system.

Alzheimer’s and Parkinson’s diseases are two neurodegenerative brain disorders affecting more than 50 million people globally. Early diagnosis and appropriate assessment of disease progression are critical for treatment and improving patients’ health. Currently, the diagnosis of these neurodegenerative diseases is based primarily on mental status exams and neuroimaging scans, which are costly, time-consuming, and sometimes erroneous. Novel, cost-effective, and precise diagnostic tools and techniques are, thus, urgently required, particularly for early detection and prediction. In the recent decade, electromagnetic imaging has evolved as a cost-effective and noninvasive alternative approach for studying brain diseases. These studies focus on wearable and portable devices and imaging algorithms. However, microwave imaging cannot detect minimal changes in the brain at early stages accurately due to its lower resolution. This article investigates machine learning (ML) techniques for the early diagnosis of acute neurological diseases, especially Alzheimer’s disease (AD). A machine-learning-based classification method is proposed. Simulations are performed on realistic numerical brain phantoms using the CST studio suite to get the scattered signals. A novel data augmentation method is proposed to generate synthetic data required for ML algorithms. A deep neural network-based autoencoder extracts features to train various ML algorithms. The classification results are compared with raw data and manual feature extraction. The study shows that the proposed ML-based method could be used to monitor AD at its early stages.

Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to 3.07 dB, a Signal-to-Mean Ratio of up to 20.78 dB and a Location Error of 1.58 mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI.

This work presents a systematic evaluation of the effectiveness of an air-operated microwave imaging (MWI) system for detection of arbitrarily oriented thin fractures in superficial bones, like the tibia. This includes the proposal of a new compact, portable setup where a single Vivaldi antenna performs a semi-cylindrical scan of the limb. The antenna is operated in monostatic radar mode, near the skin but without contact, thus ensuring hygiene and patient comfort during the exam. The image is reconstructed using a wave-migration algorithm in the frequency domain combined with an adaptative algorithm based on singular value decomposition to remove the skin artifact, dealing with non-uniform bone profile and tissue cover. The study investigates the system resolution, the robustness of the method to the uncertainty of the permittivity and thickness of the involved tested tissues, as well as the robustness to involuntary patient movement. The experimental validation was performed for the first time on an integral ex-vivo animal leg, with all tissues present, including skin and fur. It confirmed both the effectiveness of the method, and the feasibility of the setup. info:eu-repo/semantics/publishedVersion

A small ultra-wideband (UWB) patch antenna for microwave breast imaging (MWI) applications is shown off in this paper. However, to improve the antenna performance relating to the bandwidth (BW), the radiating element of the suggested antenna is modified by adding slits in the patch as well as the ground plane. The proposed prototype has a relatively small size of 20 × 19 × 1.6 mm3 and it accomplishes a return loss below −10 dB (S11< −10 dB) at an overall BW of 7 GHz (4–11 GHz) with more than 3 dBi realized gain. The antenna is designed and simulated by using a finite integration technique-based simulator. In this way, the characteristics of the fabricated antenna are measured to examine the antenna performance. Indeed, the fidelity factor of face-to-face (FtF) and side-by-side (SbS) scenarios are also noticed for the same frequency range. In the final analysis, a simulation model of the antennas, that operate as a transceiver, and a breast phantom model with tumor sample is proposed for detecting cancerous tumor cells within the breast. Hence, the proposed approach is suitable for UWB based MWI applications in tumor cell detection.

Spaceborne microwave radiometry provides an essential contribution to monitoring the Earth with varying spatial resolution both related to the reflector dimension and the frequency of operation. The ESA's Copernicus imaging microwave radiometer (CIMR) mission aims at collecting the geophysical observables at a spatial resolution ranging from 60 km in L band to 4 km in Ka band. This goal can be achieved by equipping CIMR with a large unfurlable mesh reflector antenna. A limitation of the antenna design is that the antenna pattern includes grating lobes that contaminate the scene measurement with contributions originated far from the nominal footprint. This effect introduces inaccuracies in brightness temperature measurements, particularly when facing radiometric discontinuities, e.g., near the coastlines and sea ice edges, which can be greater than the mission required maximum of 0.5 K. The aim of this article is to assess a technique which will be able to correct the effects of antenna pattern and obtain reliable TB measurements. The analyzed simple technique is based on a regularized deconvolution of the antenna pattern to reconstruct the actual brightness temperatures. The technique was tested over a synthetic scenario that mimics both steep and smooth variations in spatial and thermal domains.

Microwave imaging with large rotating angle and sparse sampling is an attractive approach to obtain the high-resolution target image with reduced radar resource. However, the popular imaging methods, e.g., Range-Doppler (RD), back projection (BP), and sparse recovery (SR), are difficult to deal with large rotating angle and sparse sampling simultaneously. In recent years, deep learning (DL) has been widely studied and been successfully used to handle the problems in computer vision. However, since most existing DL networks are put forward for the real visual image and a large amount of data is essential for network training, DL cannot be directly used to process the complex and sparse target echo for microwave imaging. In this article, a new learning imaging framework is proposed and a model-driven learning imaging network (MDLI-Net) is built for high-resolution microwave imaging with large rotating angle and sparse sampling. In the proposed framework, the electromagnetic scattering model is used to generate the training data efficiently, and the sparse microwave imaging theory is applied to guide the design of the deep imaging network. By inputting the 2-D sparse complex-valued target echo, the trained MDLI-Net can output the high-resolution and focused target image efficiently. The effectiveness of the proposed learning imaging method is validated by experiment results with both simulated and real data.

In the theoretical part of this work, a calibration strategy for both phase and amplitude calibration of rotating equispaced circular array based on redundant space calibration was introduced. It avoids the challenging implementation of conventional noise-injection networks, especially for a large-scale array, by using the strong self-consistency accomplished by array rotation. This study presents comprehensive simulation results of the proposed calibration strategy, where calibration residuals and image reconstruction errors are evaluated. The phase calibration shows outstanding performance, while the amplitude calibration is found to be biased due to the logarithms used to linearize the system of calibration equations, and the resulting image reconstruction bias is scene-dependent. A bias correction method is proposed accordingly, which requires an a priori regional brightness temperature within the observation scene. The performance of both phase and amplitude calibration take advantage of increasing the number of elements, which promises the system scalability for high spatial resolution applications., The authors would like to thank the anonymous editor and reviewers for their constructive comments and suggestions. This collaboration was also supported by the Joint Ph.D. Training Program from the University of Chinese Academy of Sciences.

While electromagnetic metasurfaces have been extensively studied for wavefront manipulations, the real three-dimensional manipulation of wavefront remains challenging. In this paper, an advanced strategy of non-interleaved multi-tasked metaplexer for three-dimensional manipulation of dual-helical electromagnetic wavefronts is reported. We first develop a general method to derive the metasurface phase distribution required to form the desired three-dimensional electric field patterns. Then, the spin-decoupled meta-atoms are designed to achieve this goal and further increase the degrees of freedom in electromagnetic manipulation. As a proof of concept, cascaded multi-plane digital images in the propagation direction are employed as the three-dimensional wavefronts, which are encoded into the spin-decoupled meta-atoms array, making the outputting circularly polarized waves carry different image information at different planes. Both the simulations and experiments verify the desirable three-dimensional wavefront manipulation capacities of our strategy for dual-helical states. Besides, this scheme can be further extended to achieve arbitrary energy allocation in three-dimensional space for dual-helical electromagnetic waves. Our strategy paves an alternative route for applications such as multiple-input multiple-output (MIMO) communications, multi-target radar detection, information storage, as well as three-dimensional microwave imaging.

This paper studies the feasibility of Microwave Imaging (MWI) for detection of fractures in superficial bones like the tibia, using a simple and practical setup. First-responders could use it for fast preliminary diagnosis in emergency locations, where X-Rays are not available. It may prove valuable also for cases where X-ray are not recommended, e.g., length pregnant women or children. The method is inspired on the synthetic aperture radar technique. A single Vivaldi antenna is used to linearly scan the bone in the 8.3-11.1 GHz frequency range and collect the scattered fields. The system is operated in air, without the need for impractical impedance-matching immersion liquids. The image is reconstructed using a Kirchhoff migration algorithm. A Singular Value Decomposition (SVD) strategy is used to remove skin and background artifacts. To test this technique, a set of full-wave simulations and experiments were conducted on a multilayer phantom and on an ex-vivo animal bone. Results show that the system can detect and locate bone transverse fractures as small as 1 mm width and 13 mm deep, even when the bone is wrapped by 2 mm thick skin. info:eu-repo/semantics/publishedVersion

A novel multiband fusion method based on a modified RELAX algorithm (MRA) is proposed. In the MRA, a maximum difference criterion is applied to the singular values of echoes' Hankel matrix to improve the accuracy of calculating the number of scattering centers. In addition, a transformation process is put forward to merge the frequency-dependent factor (FDF) term, which is in the geometrical theory of diffraction (GTD) model, into the phase term of the echo to simplify the model. Moreover, a matching relationship for the correspondence between the FDF values and the cost function is established to effectively increase the estimated precision of the FDF. Therefore, based on the MRA, exact estimations of the incoherent errors among subband echoes are achieved, and then a fused full-band echo (FBE) with high estimation accuracy is produced. Using all the fused FBEes, high-resolution, anti-non-Gaussian colored clutter microwave imaging is realized. The effectiveness of the proposed method is validated with the simulated and real-data experimental results in a simulated non-Gaussian colored clutter environment.

Aims: This work was focused on the detection of early-stage breast tumors and their location. Background: The most frequently seen disease progression and mortality in women's lives is Breast Cancer. Because of breast cancer/tumors, the risk of mortality for women has risen exponentially. From the 2020 deadline for breast cancer, women who died from carcinoma were 123.8 cases per lac women between 2006-2010. This problem is also overcome by the early identification of the tumor using different detection procedures like X-ray mammography, computerized tomography, ultrasound imaging technique, Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), microwave imaging. Researchers carried out multiple studies in these areas. Objective: To detect early-stage breast tumors and their location using SAR analysis. Methods: The major dielectrical difference between cancerous breast tissues and normal tissues in this technique is the microwave frequency range. The term Specific Absorption Rate (SAR) describes the amount of energy which is absorbed (W/kg) in the breast tissue. This segment illustrates the usefulness to diagnose the tumor position in the breast by means of maximum SAR value coordinates. Changes in breast and tumor size are important for the risk of diagnosis. The power absorbed in connecting with a normal breast and a tumor breast is measured and equivalent for different breast masses. The maximum SAR is also analyzed at distinct tumor locations at various frequency ranges. Results: It is observed that max SAR coordinates are very close to the actual tumor location. So, the maximal value of SAR coordinates indicates the existence of a tumor in the breast phantom. Conclusion: The simulated data above strongly suggests that the Max SAR values were higher in the breast phantom with tumor as compared to the breast without tumor. With different tumor radius (3 mm and 5 mm) analyzed with different resonant frequencies like 3GHz, 4GHz and 5GHz at the actual tumor location of (0, 0, 35). Even though a model representing the real properties of breast tissue is required to assess the validation of any imaging process, so the real-time development of an equivalent breast phantom and its execution is needed.

This paper presents a high-resolution inverse scattering approach using the distorted Born iterative method (DBIM) by utilizing a region-of-interest limitation scheme that assumes microwave breast cancer imaging modality. The DBIM-based tomography approach provides a quantitative dielectric profile, contributing to reliable cancer recognition, especially for dense breast media. However, if the inversion cell is small, the original DBIM will be inaccurate because the unknowns will far exceed the number of data samples. Thus, high-resolution reconstruction is a challenging issue and is not unique only to the DBIM. To address this difficulty, this paper introduces a region-of-interest (ROI)-limited scheme by developing low-resolution DBIM images. Numerical tests based on 2D FDTD demonstrate that the proposed approach provides high-resolution and accurate dielectric profiles for highly heterogeneous realistic phantoms.

There is a great need in temperature monitoring in biomedical applications. The current available temperature measurement techniques have some deficiencies in noninvasiveness, applicability, sensitivity and resolution. Microwave-induced thermoacoustic tomography (MITAT) has found diversified applications in biomedical related disciplines. It has been shown that MITAT can be used to estimate the temperature in a noninvasive manner. However, the applicability of the technique is still limited. This work presents experimental demonstration of two-dimensional (2-D) temperature detection of biological samples based on compressive TAT, referred to as MICTAT. Detailed principle of the method is established. A phantom sample and a biological sample are tested by experiments and 2-D temperature maps are obtained. This work provides a new route of getting 2-D temperature maps of biological samples and may find many applications in biomedical related areas.

This investigation presents an oval slot edge (OSE) antipodal Vivaldi antenna (AVA), hereinafter referred to as Fern AVA (FAVA), with an optimized radiation pattern. The use of OSE provided improvements in the antenna characteristics, especially regarding a lower-frequency limit reduction, an increase of the main lobe (ML) gain, and sidelobe level (SLL) reduction. Those contrasting characteristics are simultaneously obtained through the class of the Palm Tree antennas. The proposed FAVA at 1.5 GHz shows an improved gain of 6.66 dB, -12.9 dB of SLL, and 0 degrees of main lobe squint (MLS), in contrast with 4.41 dB of gain, -4.5 dB of SLL, and 4 degrees of MLS in the conventional AVA. For the FAVA, the notches in an elliptical shape, in addition to mitigating the SLL, direct the E-fields distributions towards the main lobe, which categorizes it into the same class as the Palm Tree Vivaldi Antenna. To study the performance of the proposed antenna for medical applications of near-field microwave imaging, a conceptual proof was performed, thus obtaining the microwave image of a Child Head Phantom, homogeneous and semi-realistic, being possible to detect a brain tumor.

Microwave imaging (MWI) systems are usually enclosed within casings, e.g., in order to contain the utilized coupling liquid or to help mount the antenna system. On the other hand, inverse scattering algorithms, which are used to process the measured microwave scattering data, often assume that the background medium of the imaging system extends to infinity (i.e., unbounded background medium assumption). Thus, they do not consider the reflections occurring at the system enclosure. For such algorithms to yield successful images, these reflections need to be minimized, e.g., via the use of a lossy coupling liquid. As an alternative to a lossy background medium which also reduces the desired signal level, this paper investigates the use of metallic-backed absorbing metasurfaces as the MWI system enclosure in order to (i) reduce these reflections, and also (ii) to shield the MWI system from external interference. Using simulated data, we then show that standard inverse scattering algorithms, employing the free-space assumption, can successfully process the data collected under the metasurface enclosure and yield acceptable permittivity images. The advantages and disadvantages of absorbing metasurface enclosure, along with the limitations of this study, will also be discussed. Finally, an absorbing metasurface is fabricated and its reflectivity is experimentally evaluated.

Harmonic Motion Microwave Doppler Imaging (HMMDI) method employs concurrent tissue excitation with focused ultrasound radiation force and microwaves to obtain information on malignancy, based on the dielectric and mechanical properties of the tissues. In this study, we analyze the effect of dielectric and elastic contrasts between a small (tumor) inclusion (3 mm) and the surrounding normal tissue on the HMMDI signal as functions of inclusion position and vibration frequency (from 10 Hz to 95 Hz) using simulations. To solve the forward problem of HMMDI, we developed a Discrete Dipole Approximation (DDA) based simulation method, and analyzed the received HMMDI signal for low to medium dielectric contrast (1:1.17, 1:2.6, 1:3.75) and elastic contrast (1:1.5, 1:2.5, 1:5) levels. DDA solver decreased the simulation time by a factor of 146 compared to the Finite Difference Time Domain method and facilitated the extensive number of simulations required for the analysis. The results of this study provide useful information for the development of the HMMDI method as a tool for breast cancer detection in dense tissues.

This article proposes a semijoin extreme learning machine (SJ-ELM) for super-resolution 3-D microwave imaging of objects with high contrasts. The proposed scheme develops a shallow neural network structure with the semijoin strategy to convert the scattered field data into two output channels, namely, the permittivity and the conductivity of objects, respectively. The semijoin strategy can decrease the inner matrix dimensions to reduce the computational burden for 3-D super-resolution imaging, so it is employed to connect between the nodes of the hidden layer and the output layer. The imaging performance of the proposed SJ-ELM and the conventional variational Born iterative method (VBIM) is first compared for imaging objects with different electrical sizes and contrasts, and then, different targets of imaging resolution are designed to evaluate both solvers. The proposed SJ-ELM is also assessed for imaging objects with high contrasts and experimental data and is demonstrated to have superior super-resolution imaging capabilities for high-contrast 3-D objects.

Microwave-induced thermoacoustic tomography (MITAT) is suitable for detecting foreign bodies (FBs) in human body due to the sufficient microwave contrast between the FBs and surrounding human tissues. But detection of both hot and cold FBs by the same modality has not been demonstrated. In addition, traditional widely used imaging algorithms for MITAT requires highly intensive measurements, which is not cost-effective or time-efficient. This work reports the application of a low-cost compressive thermoacoustic tomography (CTAT) system for the detection of both hot and cold FBs embedded in tissues, referred to as FBD. We give detailed description of the theory of MITAT and CTAT. We show experimental demonstrations using different shaped metallic samples as the hot FBs and glass and wood samples as the cold FBs. We achieve successful detection of all the hot and cold FBs embedded in different tissues. The CTAT-FBD method is a new paradigm promising for a bunch of diagnosis or sensing applications. It is also possible to build a portable system for this technique.

The emerging task-oriented compressed sensing (CS) provides a possibility to revolutionize traditional separate sensing-processing models by jointly considering task with data and system. This article presents an efficient mechatronic system for impact damage detection on carbon fiber reinforced polymer materials. By jointly designing the damage region segmentation task with the near-field microwave imaging system, the overall efficiency is improved by one order of magnitude without upgrading the hardware. The proposed segmentation-oriented CS greatly down-samples the specimens with a specially designed binary measurement matrix, followed by a corresponding segment-orthogonal matching pursuit process that obtains the defect region directly from under-sampled data. Compared to raster scan and the traditional CS designs, the proposed method eliminates the redundant process of obtaining the full data and defect region extraction algorithms. Segmentation-oriented CS achieves the best speed and accuracy that single method cannot, and it is much more robust to noise or measurement fault. This integrated sensing-processing method can inspire more efficient mechatronics applications.

In this work, a new technique to characterize a microwave orbital angular momentum (OAM) beam applying a single-sensor compressive imaging (SSCI) strategy is reported, which is much more time-efficient, cost-effective, and compact than traditional near-field scanning methods. A microwave spatial wave modulator (MSWM) realized by a programmable metasurface is applied to generate random mask patterns and modulate an OAM beam under test. The modulated fields are collected by a single-sensor detector. Two different algorithms can be used to reconstruct phase and magnitude profiles of the OAM beam, with the first one demands both amplitude and phase measurement while the second one only perceives the amplitude or intensity. Simulations are first carried out to investigate the effects of several parameters of the random mask patterns on the quality of reconstruction. Proof-of-concept experiments are then performed to characterize a microwave OAM beam, and obtained results show high-quality reconstructed phase and magnitude distributions of the OAM beam under test, which corroborates the effectiveness of the proposed SSCI technique. This work establishes an alternative paradigm for fast and economic measurement of microwave and millimeter-wave OAM beams. It may also facilitate OAM-based communication applications.

This isour fourth and final quarterly issue for Volume 1, 2021 of the IEEE Journal of Microwaves (JMW). We wind up our first year with 15 new articles: four invited manuscripts, nine regular contributions, and two of our Special Series pieces: Microwaves are Everywhere and Breakthroughs in Microwaves. As we plan for our future, we also look back on an exciting and satisfying inaugural year.

Microwave camera is a passive focal plane microwave imaging system which is used for large-scale, wideband electromagnetic interference (EMI) imaging. Microwave camera images the EMIs with their amplitude and location by the reflector and sensor array, like taking a photograph. This camera, however, only receives the power density of the EMIs within a wideband and the frequency information is aliased and abandoned. To address this issue, in this article, we propose a frequency estimation method to recover the frequency of the wideband EMI from its amplitude image without a need to add new hardware. For the developed imaging system, the proposed method realizes the frequency estimation in the 3–6-GHz frequency range with a resolution of 100 kHz consistently in most of the central part of the field of vision (FOV). Our proposed method is also applicable to other passive imaging applications such as passive millimeter radar imaging and infrared imaging, to extract the related information of the detected objects.

Microwave photonic (MWP) radar has the advantages of generating and processing wide bandwidth microwave signals, reconfigurability, high immunity to electromagnetic interference compared to microwave electronic radar. It has the potential to be used in applications such as intelligent autonomous and cyber-physical systems. Recent advances in microwave photonic technology led to the generation, fast processing, and control of broadband signals. Because of the advancements in photonic technologies, next-generation microwave photonic radar is becoming more prominent. This article reviews the most recent advancements and future directions in MWP radars. This review article overviews the different components of microwave photonic radar, different design challenges, and issues pertaining to it. We present a comparative study of different MWP radars on different applications. It also discusses possible future research directions of MWP radar.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

Due to physical layer compression, and consequently, the failure to produce uniform radiation patterns, it is not possible to develop fast Fourier-based image reconstruction algorithms using the raw measurements collected from metasurface antennas. An effective solution in the literature is a sub-wavelength sampling of the aperture. However, this solution is currently limited to a panel-to-probe model which requires a mechanical raster scan. On the other hand, existing works are based on time-division multiplexing. This means that only a single transmit/receive channel is active in each time slot. In this paper, we introduce a panel-to-panel model in a multistatic structure. Based on this model, two pre-processing (for two different individually measured signal and combined measured signal (CMS) scenarios) are derived to convert the raw measurements into the space-frequency domain. Then, by using the output data from the pre-processing stage and according to the configuration of the introduced imaging system, the range migration algorithm is adapted. The importance of the proposed solution for the CMS scenario is that, for the first time, it demonstrates the capability of using dynamic metasurface antennas diversity to achieve simultaneous data acquisition. The performance of the proposed approach is compared with state-of-art works in terms of reconstructed image quality and computational complexity using numerical simulations and analytical discussions.

The need for non-ionizing techniques for medical imaging applications has led to the use of microwave signals. Several systems have been introduced in recent years based on increasing the number of antennas and frequency bandwidth to obtain high resolution and good accuracy in locating objects. A novel microwave imaging system that reduces the number of required antennas for precise target location appropriate for medical applications is presented. The proposed system consists of four UWB extended gap ridge horn (EGRH) antennas covering the frequency band from 0.5 GHz to 1.5 GHz mounted on a cylindrical phantom that mimics the brain in an orthogonal set of two EGRH probes. This configuration has the ability to control both the longitudinal and transversal dimensions of the reconstructed target’s image, rather than controlling the spatial resolution, by increasing the frequency band that can be easily affected by medium losses. The system is tested numerically and experimentally by the detection of a cylindrical target within a human brain model. This work was financially supported by CICYT PID2019-107885GB-C31, and support grants for university departments and research units (FI-SDUR)(2021 FISDU 00195). This work was also supported by the National Center for Scientific and Technological Research (CNRST) under grant number PPR2/2015/36.

A low cost, with high performance, reduced graphene oxide (RGO) Ultra-wide Band (UWB) array sensor is presented to be applied with a technique of confocal radar-based microwave imaging to recognize a tumor in a human brain. RGO is used to form its patches on a Taconic substrate. The sensor functioned in a range of 1.2 to 10.8 GHz under UWB frequency. The sensor demonstrates high gain of 5.2 to 14.5 dB, with the small size of 90 mm × 45 mm2, which can be easily integrated into microwave imaging systems and allow the best functionality. Moreover, the novel UWB RGO array sensor is established as a detector with a phantom of the human head. The layers’ structure represents liquid-imitating tissues that consist of skin, fat, skull, and brain. The sensor will scan nine different points to cover the whole one-sided head phantom to obtain equally distributed reflected signals under two different situations, namely the existence and absence of the tumor. In order to accurately detect the tumor by producing sharper and clearer microwave image, the Matrix Laboratory software is used to improve the microwave imaging algorithm (delay and sum) including summing the imaging algorithm and recording the scattering parameters. The existence of a tumor will produce images with an error that is lower than 2 cm.

Holographic microwave imaging (HMI) has been proposed for early breast cancer diagnosis. Automatically classifying benign and malignant tumors in microwave images is challenging. Convolutional neural networks (CNN) have demonstrated excellent image classification and tumor detection performance. This study investigates the feasibility of using the CNN architecture to identify and classify HMI images. A modified AlexNet with transfer learning was investigated to automatically identify, classify, and quantify four and five different HMI breast images. Various pre-trained networks, including ResNet18, GoogLeNet, ResNet101, VGG19, ResNet50, DenseNet201, SqueezeNet, Inception v3, AlexNet, and Inception-ResNet-v2, were investigated to evaluate the proposed network. The proposed network achieved high classification accuracy using small training datasets (966 images) and fast training times.

This paper proposes an efficient and fast method to create large datasets for machine learning algorithms applied to brain stroke classification via microwave imaging systems. The proposed method is based on the distorted Born approximation and linearization of the scattering operator, in order to minimize the time to generate the large datasets needed to train the machine learning algorithms. The method is then applied to a microwave imaging system, which consists of twenty-four antennas conformal to the upper part of the head, realized with a 3D anthropomorphic multi-tissue model. Each antenna acts as a transmitter and receiver, and the working frequency is 1 GHz. The data are elaborated with three machine learning algorithms: support vector machine, multilayer perceptron, and k-nearest neighbours, comparing their performance. All classifiers can identify the presence or absence of the stroke, the kind of stroke (haemorrhagic or ischemic), and its position within the brain. The trained algorithms were tested with datasets generated via full-wave simulations of the overall system, considering also slightly modified antennas and limiting the data acquisition to amplitude only. The obtained results are promising for a possible real-time brain stroke classification.