Search Results (15)

Conventional respiratory monitoring is often invasive, while most non-contact technologies like radar or cameras are limited to estimating respiratory rate, failing to reconstruct the detailed waveform of the respiratory flow itself. This gap limits their clinical utility for advanced diagnostics. We introduce a novel system that bridges this gap by combining a contactless, impedance-based sensor (the Thoraxmonitor) with a dedicated machine learning framework to directly reconstruct the full respiratory flow signal. Operating at 433 $\mathrm{M}$$\mathrm{Hz}$, the system’s antenna array detects subtle changes in thoracic impedance, which are then translated into a quantitative flow signal by a Multilayer Perceptron Regressor. Based on data from 17 subjects benchmarked against a gold-standard flowmeter, our system accurately detected 98% of respiratory cycles. It achieved remarkable precision in timing respiratory events, with mean deviations of + 60 $\mathrm{m}$$\mathrm{s}$ (±79 $\mathrm{m}$$\mathrm{s}$) for inspiration and + 50 $\mathrm{m}$$\mathrm{s}$ (±63 $\mathrm{m}$$\mathrm{s}$) for expiration, making it suitable for time-critical applications. While a systematic bias in absolute tidal volume prediction currently limits inter-subject comparisons, the system excels at tracking relative intra-subject changes. Crucially, our model quantifies its own reliability, providing an intrinsic self-assessment mechanism. This work demonstrates a significant step beyond simple rate detection towards comprehensive, comfortable, and reliable respiratory analysis in clinical and everyday settings. Full article

This research presents an ultra-wideband antenna array for the non-invasive early detection of brain tumors. The primary objective of this work is to evaluate the detection capabilities of a proposed Vivaldi antenna array system for identifying small and multiple brain tumors under various simulated biological conditions. The core of the system is a Vivaldi-type antenna operating from 2.4 to 17.7 GHz, configured in both two- and four-antenna arrays. A high-fidelity, seven-layer phantom model was developed to replicate brain tissue, with each layer assigned specific electromagnetic properties (relative permittivity, tangential loss) and physical thickness. The study rigorously analyzes the system’s performance in detecting tumors across diverse scenarios, including variations in phantom complexity, tumor size, permittivity, and the number of present tumors. Using the Delay and Sum algorithm for image reconstruction, the results demonstrate the system’s feasibility in detecting tumors as small as 0.625 mm in diameter. This underscores the significant potential of the proposed design as a powerful tool for non-invasive medical diagnostics. Full article

This article presents a diagnostic method for transmit–receive modules (TRMs) in an airborne active phased array antenna (APAA). Given the spatial constraints of airborne radar systems, the diagnostic functionality was implemented using the peripheral probe method. To minimize the space, cost, and time required for modifications to the existing APAA, the side-lobe blanking (SLB) channel was employed as the probe. To prevent TRM saturation and to determine the fault detection threshold, an APAA-level test was performed using a movable anechoic chamber. The coupling level between the SLB antenna and TRM was maintained between −70 dB and −20 dB. With the result of the APAA-level test, a budget analysis on the signal path was performed, and the input attenuation level was determined. The received signal power was estimated at −40 dBm to −20 dBm. Based on the estimation, the detection threshold was determined as −50 dBm. For the operation of the diagnostic function, simple detection logic and associated control timing is implemented in the radar processor. The effectiveness of the proposed diagnostic method was validated by several test activities, including an anechoic chamber, a rooflab facility, and an actual fighter. The test result shows good agreement with the expectations. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

A non-uniform antenna array is proposed to enhance the accuracy of medical microwave imaging systems by increasing the amount of useful information captured about the imaged domain without increasing the number of antennas. These systems have so far been using uniform antenna arrays, which lead to highly correlated signals, limiting the amount of imaging information and adversely affecting diagnostic accuracy. In the proposed non-uniform antenna array method, the optimal number and positions of antennas are calculated with the aim of enhancing spatial diversity and reducing information redundancy. The mutual information coefficient is used as a metric to evaluate and minimize redundancy between received signals. A microwave head imaging system is used to verify the proposed approach. The results of the investigated scenarios show that using a non-uniform antenna configuration outperforms a uniform setup in imaging accuracy and clarity, when using the same number of antennas. Moreover, the reconstructed images demonstrate that using an optimized non-uniform antenna array with fewer elements can outperform a uniform array with more elements in terms of localization accuracy and image quality. The proposed approach improves imaging performance and reduces system complexity, cost, and power consumption, making it a practical solution for real-world biomedical imaging applications. Full article

The early diagnosis of Alzheimer’s disease remains an unmet medical need due to the cost and invasiveness of current methods. Early detection would ensure a higher quality of life for patients, enabling timely and suitable treatment. We investigate microwave sensing for low-cost, non-intrusive early detection and assessment of Alzheimer’s disease. This study is based on the emerging evidence that the electromagnetic properties of cerebrospinal fluid are affected by abnormal concentrations of proteins recognized as early-stage biomarkers. We design a conformal six-element antenna array placed on the upper portion of the head, operating in the 500 MHz to 6.5 GHz band. It measures scattering response due to changes in the dielectric properties of intracranial cerebrospinal fluid. A multi-layer perceptron network extracts the diagnostic information. Data classification consists of two steps: binary classification to identify the disease presence and multi-class classification to evaluate its stage. The algorithm is trained and validated through controlled experiments mimicking various pathological severities with an anthropomorphic multi-tissue head phantom. Results support the feasibility of the proposed method using only amplitude data and lay the foundation for more extensive studies on microwave sensing for early Alzheimer’s detection. Full article

Thermal Magnetic Resonance (ThermalMR) integrates Magnetic Resonance Imaging (MRI) diagnostics and targeted radio-frequency (RF) heating in a single theranostic device. The requirements for MRI (magnetic field) and targeted RF heating (electric field) govern the design of ThermalMR applicators. We hypothesize that helmet RF applicators (HPA) improve the efficacy of ThermalMR of brain tumors versus an annular phased RF array (APA). An HPA was designed using eight broadband self-grounded bow-tie (SGBT) antennae plus two SGBTs placed on top of the head. An APA of 10 equally spaced SGBTs was used as a reference. Electromagnetic field (EMF) simulations were performed for a test object (phantom) and a human head model. For a clinical scenario, the head model was modified with a tumor volume obtained from a patient with glioblastoma multiforme. To assess performance, we introduced multi-target evaluation (MTE) to ensure whole-brain slice accessibility. We implemented time multiplexed vector field shaping to optimize RF excitation. Our EMF and temperature simulations demonstrate that the HPA improves performance criteria critical to MRI and enhances targeted RF and temperature focusing versus the APA. Our findings are a foundation for the experimental implementation and application of a HPA en route to ThermalMR of brain tumors. Full article

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples. Full article

Thermal Magnetic Resonance (ThermalMR) is a theranostic concept that combines diagnostic magnetic resonance imaging (MRI) with targeted thermal therapy in the hyperthermia (HT) range using a radiofrequency (RF) applicator in an integrated system. ThermalMR adds a therapeutic dimension to a diagnostic MRI device. Focused, targeted RF heating of deep-seated brain tumors, accurate non-invasive temperature monitoring and high-resolution MRI are specific requirements of ThermalMR that can be addressed with novel concepts in RF applicator design. This work examines hybrid RF applicator arrays combining loop and self-grounded bow-tie (SGBT) dipole antennas for ThermalMR of brain tumors, at magnetic field strengths of 7.0 T, 9.4 T and 10.5 T. These high-density RF arrays improve the feasible transmission channel count, and provide additional degrees of freedom for RF shimming not afforded by using dipole antennas only, for superior thermal therapy and MRI diagnostics. These improvements are especially relevant for ThermalMR theranostics of deep-seated brain tumors because of the small surface area of the head. ThermalMR RF applicators with the hybrid loop+SGBT dipole design outperformed applicators using dipole-only and loop-only designs, with superior MRI performance and targeted RF heating. Array variants with a horse-shoe configuration covering an arc (270°) around the head avoiding the eyes performed better than designs with 360° coverage, with a 1.3 °C higher temperature rise inside the tumor while sparing healthy tissue. Our EMF and temperature simulations performed on a virtual patient with a clinically realistic intracranial tumor provide a technical foundation for implementation of advanced RF applicators tailored for ThermalMR theranostics of brain tumors. Full article

This study assesses the efficacy of detecting colorectal cancer precursors or polyps in an ex vivo human colon model with a microwave colonoscopy algorithm. Nowadays, 22% of polyps go undetected with conventional colonoscopy, and the risk of cancer after a negative colonoscopy can be up to 7.9%. We developed a microwave colonoscopy device that consists of a cylindrical ring-shaped switchable microwave antenna array that can be attached to the tip of a conventional colonoscope as an accessory. The accessory is connected to an external unit that allows successive measurements of the colon and processes the measurements with a microwave imaging algorithm. An acoustic signal is generated when a polyp is detected. Fifteen ex vivo freshly excised human colons with cancer (n = 12) or polyps (n = 3) were examined with the microwave-assisted colonoscopy system simulating a real colonoscopy exploration. After the experiment, the dielectric properties of the specimens were measured with a coaxial probe and the samples underwent a pathology analysis. The results show that all the neoplasms were detected with a sensitivity of 100% and specificity of 87.4%. Full article

Injuries to the hamstring muscles are an increasing problem in sports. Imaging plays a key role in diagnosing and managing athletes with muscle injuries, but there are several problems with conventional imaging modalities with respect to cost and availability. We hypothesized that microwave imaging could provide improved availability and lower costs and lead to improved and more accurate diagnostics. In this paper, a semicircular microwave imaging array with eight antennae was investigated. A key component in this system is the novel antenna design, which is based on a monopole antenna and a lossy gel. The purpose of the gel is to reduce the effects of multipath signals and improve the imaging quality. Several different gels have been manufactured and evaluated in imaging experiments. For comparison, corresponding simulations were performed. The results showed that the gels can effectively reduce the multipath signals and the imaging experiments resulted in significantly more stable and repeatable reconstructions when a lossy gel was used compared to when an almost non-lossy gel was used. Full article

The design of a microelectromechanical systems (MEMS) ultra-wideband (UWB) RMS power sensor is presented. The sensor incorporates a microfabricated Fe-Co-B core planar inductor and a microfabricated vibrating diaphragm variable capacitor on adhesively bonded glass wafers in a footprint area of 970 × 970 µm² to operate in the 3.1–10.6 GHz UWB frequency range. When exposed to a far-field UWB electromagnetic radiation, the planar inductor acts as a loop antenna to generate a frequency-independent voltage across the MEMS capacitor. The voltage generates a coulombic attraction force between the diaphragm and backplate that deforms the diaphragm to change the capacitance. The frequency-independent capacitance change is sensed using a transimpedance amplifier to generate an output voltage. The sensor exhibits a linear capacitance change induced voltage relation and a calculated sensitivity of 4.5 aF/0.8 µA/m. The sensor can be used as a standalone UWB power sensor or as a 2D array for microwave-based biomedical diagnostic imaging applications or for non-contact material characterization. The device can easily be tailored for power sensing in other application areas such as, 5G, WiFi, and Internet-of-Things (IoT). The foreseen fabrication technique can rely on standard readily available microfabrication techniques. Full article

This paper reports the development of a new composite material as a matching medium for medical microwave diagnostic systems, where maximizing the microwave energy that penetrates the interrogated tissue is critical for improving the quality of the diagnostic images. The proposed material has several advantages over what is commonly used in microwave diagnostic systems: it is semi-flexible and rigid, and it can maximize microwave energy coupling by matching the tissue’s dielectric constant without introducing high loss. The developed matching medium is a mirocomposite of barium titanate filler in polydimethylsiloxane (PDMS) in different weight-based mixing ratios. Dielectric properties of the material are measured using a Keysight open-ended coaxial slim probe from 0.5 to 10 GHz. To avoid systematic errors, a full dielectric properties calibration is performed before measurements of sample materials. Furthermore, the repeatability of the measurements and the homogeneity of the sample of interest are considered. Finally, to evaluate the proposed matching medium, its impact on a printed monopole antenna is studied. We demonstrate that the permittivity of the investigated mixtures can be increased in a controlled manner to reach values that have been previously shown to be optimal for medical microwave imaging (MWI) such as stroke and breast cancer diagnostic applications. As a result, the material is a good candidate for supporting antenna arrays designed for portable MWI scanners in applications such as stroke detection. Full article

The solution of inverse source problems by numerical procedures requires the investigation of the number of independent pieces of information that can be reconstructed stably. To this end, the mathematical properties of the relevant operators are to be examined in connection with the source shape. The aim of this work is to investigate the effect of the source shape on the eigendecomposition of the radiation operator in a 2D geometry, when the radiated field is observed over a semi-circumference in the far zone. We examine both the behavior of the eigenvalues and the effect of the choice of the representation variables on the point spread function (PSF). In particular, the effect of the choice of the representation variables is considered since operator properties may depend on it. We analyze different source shapes evolving from a line to a semi-ellipse and, finally, to a semi-circumference, in order to understand how the increase of the source aspect ratio affects the results. The main conclusions concern an estimate of the number of degrees of freedom in connection with the source geometry and the fact that the PSF exhibits the same variant behavior along the considered domain, independently of the observation variable. The practical relevance of the result is illustrated by two numerical examples. The first one deals with the conformal array diagnostics for the reliable reconstruction of the excitation of the array elements. The second one concerns the array synthesis problem, and a comparison between the radiating performances of the source geometries is presented. Full article

In this paper, a diagnostic tool or procedure based on Bayesian compressive sensing (BCS) is proposed for identification of failed element(s) which manifest in millimeter-wave planar antenna arrays. With adequate a priori knowledge of the reference antenna array radiation pattern, a diagnostic problem of faulty elements was formulated. Sparse recovery algorithms, including total variation (TV), mixed ${\ell }_1/{\ell }_2$ norm, and minimization of the ${\ell }_1$ , are readily available in the literature, and were used to diagnose the array under test (AUT) from measurement points, consequently providing faster and better diagnostic schemes than the traditional mechanisms, such as the back propagation algorithm, matrix method algorithm, etc. However, these approaches exhibit some drawbacks in terms of effectiveness and reliability in noisy data, and a large number of measurement data points. To overcome these problems, a methodology based on BCS was adapted in this paper. From far-field radiation pattern samples, planar array diagnosis was formulated as a sparse signal recovery problem where BCS was applied to recover the locations of the faults using relevance vector machine (RVM). The resulted BCS approach was validated through simulations and experiments to provide suitable guidelines for users, as well as insight into the features and potential of the proposed procedure. A Ka-band $\left(28.9\mathrm{GHz}\right)$ $10\times 10$ rectangular microstrip patch antenna array that emulates failure with zero excitation was designed for far-field measurements in an anechoic chamber. Both simulated and measured far-field samples were used to test the proposed approach. The proposed technique is demonstrated to detect diagnostic problems with fewer measurements provided the prior knowledge of the array radiation pattern is known, and the number of faults is relatively smaller than the array size. The effectiveness and reliability of the technique is verified experimentally and via simulation. In addition to a faster diagnosis and better reconstruction accuracy, the BCS-based technique shows more robustness to additive noisy data compared to other compressive sensing methods. The proposed procedure can be applied to next-generation transceivers, aerospace systems, radar systems, and other communication systems. Full article