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

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

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

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

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

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

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

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]

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

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]

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

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

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

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

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

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

The 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]

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

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

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

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.

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

Digital coding metasurfaces composed of subwavelength meta-atoms can flexibly control electromagnetic waves to achieve holography, which has great potential in millimeter-wave imaging systems and data storage. In this paper, we propose a 3-bit reflective digital coding metasurface. The incident linearly polarized waves can be transformed into cross-polarized components with distinct phase responses by adjusting the rotational and open angles of the coding elements. The 3-bit phase performance can be retained over a wide bandwidth from 12 to 18 GHz by simultaneously changing the rotational and open angles. Based on the proposed broadband metasurface, broadband holography is successfully demonstrated with the optimization of a modified Gerchberg–Saxton algorithm. As a proof of concept, five schemes with different holograms integrating the letters "S," "E," "U," "X," and "Z" are simulated from 12 to 18 GHz. Good simulation results validate the performance of the proposed broadband holography, showing a relative bandwidth of 40%. Two prototypes superposing the holograms of letters "U" and "X" are fabricated and measured in a near-field microwave anechoic chamber. The experimental results corroborate well with simulated results, further supporting the demonstration. We believe that the proposed broadband holography based on the digital coding metasurface paves a way to wideband applications for microwave imaging, information processing, and holographic data storage. [ABSTRACT FROM AUTHOR]

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

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]

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

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

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]

This article proposes a low‐profile ultra‐wideband (UWB) antenna for breast tumor detection using the monostatic radar‐based microwave imaging (RBMI) technique. The proposed UWB antenna consists of a circular patch and ground plane along with a modified tapered feed line having a total size of 13 × 9 × 1.6 mm3. The designed antenna operates from 4 to 11 GHz based on a −10 dB reflection coefficient with a peak gain of 9.5 dBi at 4.8 GHz. Experimental validation is carried out after fabricating the prototype of the UWB antenna and the breast mimic. The measured backscattered signals are captured using the vector network analyzer (E‐5063A) by keeping the antenna at 10 mm from the breast phantom. After that, post‐processing of the measured data is done to detect the exact depth and location of the malignant tissue. The proposed antenna uses UWB band frequency of operation because it has some unique features, that is, it provides immunity to multipath fading, has a simple design, has a low cost of fabrication, and has biological friendliness. Moreover, due to the proposed antenna's resonance at a lower resonant frequency better depth of penetration is achieved in human body phantoms and the proposed antenna provides specific absorption rates (SAR) of 0.877 W/kg over 1 g of tissue at 4.8 GHz, which is an acceptable limit for SAR as per FCC guidelines. Therefore, the proposed antenna and monostatic RBMI technique are good candidates for breast tumor detection. [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]

Background: Microwave imaging has been proposed for medical applications, creating maps related to water content of tissues. Breast imaging has emerged as a key application because the signals can be coupled directly into the breast and experience limited attenuation in fatty tissues. While the literature contains reports of tumor detection with microwave approaches, there is limited exploration of treatment monitoring. Purpose: This study aims to detect treatment‐related changes in breast tissue with a low‐resolution microwave scanner. Methods: Microwave scans of 15 patients undergoing treatment for early‐stage breast cancer are collected at up to 4 time points: after surgery (baseline), 6 weeks after accelerated partial breast radiation, as well as 1 and 2 years post‐treatment. Both the treated and untreated breast are scanned at each time point. The microwave scanner consists of planar transmit and receive arrays and uses signals from 0.1 to 10 GHz. The average microwave frequency properties (permittivity) are calculated for each scan to enable quantitative comparison. Baseline and 6‐week results are analyzed with a two‐way ANOVA with blocking. Results: Consistent properties are observed for the untreated breast over time, similar to a previous study. Comparison of the scans of the treated and untreated breast suggests increased properties related to treatment, particularly at baseline and 6‐weeks following radiotherapy. Analysis of the average properties of the scans with ANOVA indicates statistically significant differences (p<0.05$p < 0.05$) in the treated and untreated breast at these time points. Conclusions: Microwave imaging has the potential to track treatment‐related changes in breast tissues. [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]

A compact co-planar waveguide (CPW) based triple-band printed conformal antenna for on-body applications is presented in this research. In order to accomplish impedance matching, a coplanar structure is employed with the ground on the same plane, and this is printed on the top layer of a polyimide substrate of 0.6 mm thickness. The proposed conformal antenna consists of two identical patch elements. The radiating element is made up of two split ring resonators positioned on the top of the feeding element. The suggested on-body antenna has an overall miniaturized size of only 35 37.5 mm², making it ideal for use in the biomedical field. The designed antenna is capable to cover various spectrum spanning from 2.387-2.618 GHz, 4.2-4.64 GHz, and 6.23-6.56 GHz with a 2:1 voltage standing wave ratio. The designed antenna is fabricated and antenna parameters are measured which is correlated with simulated results. The antenna achieves a total gain of around 5 dBi with radiation efficiency of up to 95 % over the various operating spectrums. To validate the conformality over curved surfaces, the fabricated prototype of the proposed antenna is bent at 30°, 45, 90° angles and the measured results are compared with simulation results of the bending analysis. Furthermore, to validate the on-body performance of the antenna Specific Absorption Rate has been calculated through simulation and it is found to be lower than 1 W/Kg over all three bands of spectrums which proves that the proposed bio-medical antenna could be a viable candidate for microwave imaging applications. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate a multi-functional metasurface for microwave beam-shaping application. The metasurface consists of an array of programmable unit cells, and each unit cell is integrated with one varactor diode. By turning the electrical bias on the diode on and off, the phase delay of the microwave reflected by the metasurface can be switched between 0 and π at a 6.2 GHz frequency, which makes the metasurface 1-bit-coded. By programming the 1-bit-coded metasurface, the generation of a single-focus beam, a double-focus beam and a focused vortex beam was experimentally demonstrated. Furthermore, the single-focus beam with tunable focal lengths of 54 mm, 103 mm and 152 mm was experimentally observed at 5.7 GHz. The proposed programmable metasurface manifests robust and flexible beam-shaping ability which allows its application to microwave imaging, information transmission and sensing applications. [ABSTRACT FROM AUTHOR]

We present the design and the performance evaluation of a new interconnect for large-scale densely packed electronically scanned antenna arrays that utilize a high-speed digital board-to-board vertical connector. The application targets microwave tissue, imaging in the frequency range from 3 GHz to 8 GHz. The tissue-imaging arrays consist of hundreds of active antenna elements, which require low-reflection, low-loss, and low-crosstalk connections to their respective receiving and transmitting circuits. The small antenna size and the high array density preclude the use of coaxial connectors, which are also expensive and mechanically unreliable. Modern board-to-board high-speed connectors promise bandwidths as high as 12 GHz, along with high pin density, mechanical robustness, and low cost. However, their compatibility with the various transmission lines leading to/from the miniature printed antenna elements and microwave circuitry is not well studied. Here, we focus on the design of the transitions from coplanar waveguide transmission lines to/from a high-speed vertical connector. The performance of the interconnect is examined through electromagnetic simulations and measurements. Comparison is carried out with the expensive sub-miniature push-on sub-micro coaxial connectors commonly used in miniature radio-frequency electronics. The results demonstrate that high-speed vertical connectors can provide comparable performance in the UWB frequency range. [ABSTRACT FROM AUTHOR]

This work presents a study where a Sinusoidal Corrugated Antipodal Vivaldi Antenna (SC-AVA) operating in the Ultra-Wideband (UWB) region is employed as a transducer for microwave imaging of a cancerous breast. The functionality of the antenna within the UWB range is initially confirmed through thorough testing of performance parameters, including return loss, gain, radiation pattern, and group delay. Subsequently, its practical application in biomedical imaging is evaluated by measuring Specific Absorption Rate (SAR) readings at multiple frequencies within the operational range. The SAR readings are obtained from an EM simulator by modelling both homogeneous and heterogeneous breast phantoms and placing them in close proximity to the transducer. The SAR values are recorded at various frequencies, and it is determined that these readings comply with the Federal Communication Commission (FCC) regulations. The modelled SC-AVA is further utilized in the detection of a single tumor in a homogeneous breast phantom and multiple tumors in a realistic heterogeneous breast phantom. These phantoms are developed in a laboratory environment and imaged using an in-house developed monostatic microwave imaging setup. To gather preliminary information about the target, a homogeneous phantom with one tumor is imaged initially. Subsequently the heterogeneous phantom with two embedded tumors is imaged in this study. The imaging results demonstrate that tumors of different sizes can be clearly visualized in both breast phantoms using the SC-AVA, employing image reconstruction algorithms such as Delay and Sum (DAS) and iterative Delay and Sum (it-DAS). Furthermore, a comparison of the reconstructed images reveals that the it-DAS reconstruction algorithm produces images with improved clarity compared to the DAS algorithm. [ABSTRACT FROM AUTHOR]

The monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters. [ABSTRACT FROM AUTHOR]

Microwave imaging is one of the rapidly developing frontier disciplines in the field of modern medical imaging. The development of microwave imaging algorithms for reconstructing stroke images is discussed in this paper. Compared with traditional stroke detection and diagnosis techniques, microwave imaging has the advantages of low price and no ionizing radiation hazards. The research hotspots of microwave imaging algorithms in the field of stroke are mainly reflected in the design and improvement of microwave tomography, radar imaging, and deep learning imaging. However, the current research lacks the analysis and combing of microwave imaging algorithms. In this paper, the development of common microwave imaging algorithms is reviewed. The concept, research status, current research hotspots and difficulties, and future development trends of microwave imaging algorithms are systematically expounded. The microwave antenna is used to collect scattered signals, and a series of microwave imaging algorithms are used to reconstruct the stroke image. The classification diagram and flow chart of the algorithms are shown in this Figure. (The classification diagram and flow chart are based on the microwave imaging algorithms.) [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is an active microwave imaging sensor for high-resolution observation, with the ability of working in all-weather and all-day. Recently, SAR images have been widely used in many fields. Among them, ship detection in single-channel SAR images is a significant part of civilian and military fields. This article first discusses the characteristic of SAR images and the detectability of ships, then summarizes the recent advance of traditional and deep learning-based methods used for ship detection in single-channel SAR images. In addition, the characteristics and existing problems of various methods are discussed and their future development trends are predicted. Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD). [ABSTRACT FROM AUTHOR]

A major eruptive flare occurred on 12 January 2022 in the northeast not far behind the solar limb (N32 E116). The eruption produced a fast coronal mass ejection (CME). The rising ejecta was observed by the telescopes in the extreme ultraviolet and by the multi-frequency Siberian Radioheliograph (SRH) in the 5.8 – 11.8 GHz range. We show how the slope of the decrease in the brightness temperature of the rising ejecta, measured from the microwave SRH images, is related to the heat inflow or outflow in its body during rapid expansion with high acceleration and under the assumption that the plasma ionization state changes insignificantly within the measurement interval. We found that the low-temperature plasma component in the erupting prominence underwent heating. Most likely, this was due to the predominance of ohmic heating because i) the polytropic index of expanding plasma expected in this case was closest to the experimentally measured one, and ii) the ohmic dissipation due to electron-proton collisions loses its efficiency during expansion much slower than the other mechanisms of heating or cooling. [ABSTRACT FROM AUTHOR]

Background: microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. Methods: this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. Results: the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model's ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). Conclusion: overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain. [ABSTRACT FROM AUTHOR]

Planar antennas have become an integral component in modern biomedical instruments owing to their compact structure, cost effectiveness, and light weight. These antennas are crucial in realizing medical systems such as body area networks, remote health monitoring, and microwave imaging systems. Antennas intended for the above applications should be conformal and fabricated using lightweight materials that are suitable for wear on the human body. Wearable antennas are intended to be placed on the human body to examine its health conditions. Hence, the performance of the antenna, such as its radiation characteristics across the operating frequency bands, should not be affected by human body proximity. This is achieved by selecting appropriate conformal materials whose characteristics remain stable under all environmental conditions. This paper aims to highlight the effects of human body proximity on wearable antenna performance. Additionally, this paper reviews the various types of flexible antennas proposed for biomedical applications. It describes the challenges in designing wearable antennas, the selection of a flexible material that is suitable for fabricating wearable antennas, and the relevant methods of fabrication. This paper also highlights the future directions in this rapidly growing field. Flexible antennas are the keystone for implementing next-generation wireless communication devices for health monitoring and health safety applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel ultra-wideband microwave antenna system to detect breast cancer and estimate tumor diameter. The system operates within the frequency range of 1 to 12 GHz and comprises a microstrip-fed monopole antenna that encircles the breast to identify the presence of tumors. The study demonstrates that a tumor within the breast can be detected by observing changes in the distribution of current density within the breast tissue, particularly in regions containing tumors of varying sizes. The research findings reveal that the system can identify breast tumors with the highest recorded current density of 188 A/m2 in cases with a tumor diameter of 30 mm, while the lowest recorded current density is 140 A/m2 for tumors with a diameter of 5 mm. Furthermore, the highest Specific Absorption Rate (SAR) value measured at the surface of the breast model is 0.2 W/kg. To determine the diameter of the tumors, the system collects and analyzes backscattered waves from a breast model. The investigation covers tumors with diameters ranging from 1 mm to 35 mm, and the received signals are recorded. In contrast to prior research, this study introduces an empirical model with a remarkable accuracy rate of 92.28% for characterizing the diameter of breast tumors based on the measurement analysis. [ABSTRACT FROM AUTHOR]