We present a biocompatible high-contrast low-loss antenna (HCLA) designed for efficient into-body radiation for applications as diverse as medical telemetry, sensing, and imaging. The HCLA is wearable with a compact size of 2.62 cm and operates across the 1 to 5 GHz bandwidth. The quasi-bowtie antenna is loaded with a high-contrast (i.e., alternating layers of high and low permittivity materials) and low-loss dielectric to improve directivity and gain into the biological tissues. Measurement results at 2.4 GHz are in good agreement with simulations and show 5.72 dB improvement in transmission loss over the most efficient into-body radiator reported in the past. At the high end of the frequency bandwidth, simulation results for two antennas placed across each other with tissue in between show ~12.5 dB improvement in transmission loss. The HCLA is fabricated with stable, low-loss materials that allow for repeatability and consistency in the fabrication process, thus, addressing limitations of the current state-of-the-art. It is also made from biocompatible materials that enable it to be placed directly on the skin for real-world implementation. In this paper, we discuss the operation principle and design of the HCLA, its transmission performance, radiation patterns, and specific absorption rate.

We investigated how to construct low-order subspace basis sets to accurately represent electromagnetic fields generated within inhomogeneous arbitrary objects by radio-frequency sources external to a Huygen's surface. The basis generation relies on the singular value decomposition of Green's functions integro-differential operators which makes it feasible to derive a reduced-order yet stable model. We present a detailed study of the theoretical and numerical requisites for generating such basis, and show how it can be used to calculate performance limits in magnetic resonance imaging applications. Finally, we propose a novel numerical framework for the computation of characteristic modes of arbitrary inhomogeneous objects. We validated accuracy and convergence properties of the numerical basis against a complete analytical basis in the case of a uniform spherical object. We showed that the discretization of the Huygens's surface has a minimal effect on the accuracy of the calculations, which mainly depended on the electromagnetic solver resolution and order of approximation.

In order to conduct the research of machine-learning (ML) based microwave breast imaging (MBI), a large number of digital dielectric breast phantoms that can be used as training data (ground truth) are required but are difficult to be achieved from practice. Although a few dielectric breast phantoms have been developed for research purpose, the number and the diversity are limited and is far inadequate to develop a robust ML algorithm for MBI. This paper presents a neural network method to generate 2D virtual breast phantoms that are similar to the real ones, which can be used to develop ML-based MBI in the future. The generated phantoms are similar but are different from those used in training. Each phantom consists of several images with each representing the distribution of a dielectric parameter in the breast map. Statistical analysis was performed over 10,000 generated phantoms to investigate the performance of the generative network. With the generative network, one may generate unlimited number of breast images with more variations, so the ML-based MBI will be more ready to deploy.

A butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the construction cost of the HOD-BF-compressed system matrix and inversion cost for the preconditioner, where is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

In this work, we propose a method for the compression of the coupling matrix in volume-surface integral equation (VSIE) formulations. VSIE methods are used for electromagnetic analysis in magnetic resonance imaging (MRI) applications, for which the coupling matrix models the interactions between the coil and the body. We showed that these effects can be represented as independent interactions between remote elements in 3D tensor formats, and subsequently decomposed with the Tucker model. Our method can work in tandem with the adaptive cross approximation technique to provide fast solutions of VSIE problems. We demonstrated that our compression approaches can enable the use of VSIE matrices of prohibitive memory requirements, by allowing the effective use of modern graphical processing units (GPUs) to accelerate the arising matrix-vector products. This is critical to enable numerical MRI simulations at clinical voxel resolutions in a feasible computation time. In this paper, we demonstrate that the VSIE matrix-vector products needed to calculate the electromagnetic field produced by an MRI coil inside a numerical body model with 1 mm voxel resolution, could be performed in ~ 33 seconds in a GPU, after compressing the associated coupling matrix from ~ 80 TB to ~ 43 MB.

A new comparison-to-reference performance verification technique compares an E-band channel-sounder and reference vector network analyzer measurements of the same controlled, static channel. This new technique reduces the number of inaccurate assumptions that exist in other methods providing a stronger verification of the channel-sounder hardware and processing performance. This technique compares the channel-sounder and VNA derived channel metrics from these measurements. Using mechanical switches, we established a controlled, static RF channel. The vector network analyzer has a comprehensive uncertainty analysis that propagates systematic and random uncertainties through to the power delay profiles. The method is suitable for millimeter-wave channel-sounder hardware with removable antennas.

Microwave image reconstruction based on a deep-learning method is investigated in this paper. The neural network is capable of converting measured microwave signals acquired from a 24×24 antenna array at 4 GHz into a 128×128 image. To reduce the training difficulty, we first developed an autoencoder by which high-resolution images (128×128) were represented with 256×1 vectors; then we developed the second neural network which aimed to map microwave signals to the compressed features (256×1 vector). Two neural networks can be combined to a full network to make reconstructions, when both are successfully developed. The present two-stage training method reduces the difficulty in training deep learning networks (DLN) for inverse reconstruction. The developed neural network is validated by simulation examples and experimental data with objects in different shapes/sizes, placed in different locations, and with dielectric constant ranging from 2~6. Comparisons between the imaging results achieved by the present method and two conventional approaches: distorted Born iterative method (DBIM) and phase confocal method (PCM) are also provided.

In the paper, "Investigation of histology region in dielectric measurements of heterogeneous tissues," by Porter and O'Halloran, the authors utilize a flexible phantom in a layered material dielectric property analysis to quantify the effective sensing volume of a coaxial dielectric probe. Ostensibly, this test has been used by others to characterize the region for which percent variation in the material composition in front of the probe corresponds to percent variation in the computed effective dielectric properties. By employing a compressible material, the authors fail to isolate features that are attributable solely to the probe, itself, and inadvertently incorporate confounding characteristics associated with the compressible nature of the material. The net effect is to exaggerate the probe's sensing volume which undermines conclusions drawn from the subsequent tissue dielectric property studies.

At the ultrahigh frequencies common to portable radios, tunnels such as mine entries are often modeled by hollow dielectric waveguides. The roughness condition of the tunnel walls has an influence on radio propagation, and therefore should be taken into account when an accurate power prediction is needed. This paper investigates how wall roughness affects radio propagation in tunnels, and presents a unified ray tracing and modal method for modeling radio propagation in tunnels with rough walls. First, general analytical formulas for modeling the influence of the wall roughness are derived, based on the modal method and the ray tracing method, respectively. Second, the equivalence of the ray tracing and modal methods in the presence of wall roughnesses is mathematically proved, by showing that the ray tracing-based analytical formula can converge to the modal-based formula through the Poisson summation formula. The derivation and findings are verified by simulation results based on ray tracing and modal methods.

A free-space measurement method is presented for the characterization of low-loss dielectric materials at millimeter-wave frequencies that does not require any assumption of knowledge of the sample thickness. The method first employs only maximal and minimal envelopes of measured transmission scattering parameters to determine the real part of the permittivity of test materials. Subsequently, the thickness of the sample is estimated from and frequencies for maximal and minimal peaks of the transmission scattering parameter. The calculation of the imaginary part of the permittivity then easily follows. Our method is examined by measuring two cross-linked polystyrene samples, one polytetrafluoroethylene sample and one polymethylpentene sample in the frequency range of 220-325 GHz at the incident angles of 0°, 10°, 20°, and 30°. Moreover, an explicit uncertainty analysis for the permittivity is derived, and uncertainties of the extracted complex permittivity are reported.

We present a focal-beamforming-enhanced formulation of the distorted Born iterative method (DBIM) for microwave breast imaging. Incorporating beamforming into the imaging algorithm has the potential to mitigate the effect of noise on the image reconstruction. We apply the focal-beamforming-enhanced DBIM algorithm to simulated array measurements from two MRI-derived, anatomically realistic numerical breast phantoms and compare its performance to that of the DBIM formulated with two non-focal schemes. The first scheme simply averages scattered field data from reciprocal antenna pairs while the second scheme discards reciprocal pairs. Images of the dielectric properties are reconstructed for signal-to-noise ratios (SNR) ranging from 35 dB down to 0 dB. We show that, for low SNR, the focal beamforming algorithm creates reconstructions that are of higher fidelity with respect to the exact dielectric profiles of the phantoms as compared to reconstructions created using the non-focal schemes. At high SNR, the focal and non-focal reconstructions are of comparable quality.

We present a comprehensive study of a class of multi-band miniaturized patch antennas designed for use in a 3D enclosed sensor array for microwave breast imaging. Miniaturization and multi-band operation are achieved by loading the antenna with non-radiating slots at strategic locations along the patch. This results in symmetric radiation patterns and similar radiation characteristics at all frequencies of operation. Prototypes were fabricated and tested in a biocompatible immersion medium. Excellent agreement was obtained between simulations and measurements. The trade-off between miniaturization and radiation efficiency within this class of patch antennas is explored via a numerical analysis of the effects of the location and number of slots, as well as the thickness and permittivity of the dielectric substrate, on the resonant frequencies and gain. Additionally, we compare 3D quantitative microwave breast imaging performance achieved with two different enclosed arrays of slot-loaded miniaturized patch antennas. Simulated array measurements were obtained for a 3D anatomically realistic numerical breast phantom. The reconstructed breast images generated from miniaturized patch array data suggest that, for the realistic noise power levels assumed in this study, the variations in gain observed across this class of multi-band patch antennas do not significantly impact the overall image quality. We conclude that these miniaturized antennas are promising candidates as compact array elements for shielded, multi-frequency microwave breast imaging systems.

This paper introduces an iterative scheme to overcome the unresolved issues presented in S-FDTD (stochastic finite-difference time-domain) for obtaining ensemble average field values recently reported by Smith and Furse in an attempt to replace the brute force multiple-realization also known as Monte-Carlo approach with a single-realization scheme. Our formulation is particularly useful for studying light interactions with biological cells and tissues having sub-wavelength scale features. Numerical results demonstrate that such a small scale variation can be effectively modeled with a random medium problem which when simulated with the proposed S-FDTD indeed produces a very accurate result.

Three-dimensional microwave tomography represents a potentially very important advance over 2D techniques because it eliminates associated approximations which may lead to more accurate images. However, with the significant increase in problem size, computational efficiency is critical to making 3D microwave imaging viable in practice. In this paper, we present two 3D image reconstruction methods utilizing 3D scalar and vector field modeling strategies, respectively. Finite element (FE) and finite-difference time-domain (FDTD) algorithms are used to model the electromagnetic field interactions in human tissue in 3D. Image reconstruction techniques previously developed for the 2D problem, such as the dual-mesh scheme, iterative block solver, and adjoint Jacobian method are extended directly to 3D reconstructions. Speed improvements achieved by setting an initial field distribution and utilizing an alternating-direction implicit (ADI) FDTD are explored for 3D vector field modeling. The proposed algorithms are tested with simulated data and correctly recovered the position, size and electrical properties of the target. The adjoint formulation and the FDTD method utilizing initial field estimates are found to be significantly more effective in reducing the computation time. Finally, these results also demonstrate that cross-plane measurements are critical for reconstructing 3D profiles of the target.

We investigate solving the electromagnetic inverse scattering problem using the distorted Born iterative method (DBIM) in conjunction with a variable-selection approach known as the elastic net. The elastic net applies both ℓ1 and ℓ2 penalties to regularize the system of linear equations that result at each iteration of the DBIM. The elastic net thus incorporates both the stabilizing effect of the ℓ2 penalty with the sparsity encouraging effect of the ℓ1 penalty. The DBIM with the elastic net outperforms the commonly used ℓ2 regularizer when the unknown distribution of dielectric properties is sparse in a known set of basis functions. We consider two very different 3-D examples to demonstrate the efficacy and applicability of our approach. For both examples, we use a scalar approximation in the inverse solution. In the first example the actual distribution of dielectric properties is exactly sparse in a set of 3-D wavelets. The performances of the elastic net and ℓ2 approaches are compared to the ideal case where it is known a priori which wavelets are involved in the true solution. The second example comes from the area of microwave imaging for breast cancer detection. For a given set of 3-D Gaussian basis functions, we show that the elastic net approach can produce a more accurate estimate of the distribution of dielectric properties (in particular, the effective conductivity) within an anatomically realistic 3-D numerical breast phantom. In contrast, the DBIM with an ℓ2 penalty produces an estimate which suffers from multiple artifacts.

The effect of a user's proximity on wireless device performance is critical to test the device under realistic conditions. In this work, we propose and demonstrate an improved uncertainty estimation method for antenna efficiency measurements in a reverberation chamber. The improved method separately computes uncertainties due to the effects of chamber loading by a phantom and the effects of antenna mismatch introduced by this phantom, illustrating the sensitivity of uncertainty to close-proximity user effects. We demonstrate that, while the impact of the phantom may be significant on antenna efficiency, and, it has some influence on the uncertainty in the measurement, its impact on overall uncertainty may be insignificant. This is demonstrated using the two-antenna method in the presence of a phantom close to the antenna under test. We illustrate the method by summarizing the antenna efficiencies with their uncertainties and the impact of the phantom for important communication bands. Due to the large effect of the user on antenna performance, this type of measurement and its uncertainty evaluation is a valuable way to characterize antenna efficiency including user effects.

Millimeter-wave transceivers will feature massive phased-array antennas whose pencilbeams can be steered toward the angle of arrival of the propagation path having the maximum power, exploiting their high gain to compensate for the greater path loss witnessed in the upper spectrum. For this reason, maximum-power path-loss models, in contrast to conventional ones based on the integrated power from an omnidirectional antenna, may be more relevant. Yet to our knowledge, they do not appear in the literature save for one reference. In this paper, we compare both model types at 83.5 GHz for four indoor environments typical of hotspot deployments in line-of-sight (LOS) and non-LOS conditions up to a range of 160 m. To fit the models, we conducted a measurement campaign with over 3000 different transmitter-receiver configurations using a custom-designed channel sounder capable of extracting the delay and 3-D angle of arrival of the received paths with super-resolution. The models are supported by a detailed analysis of the propagation mechanisms of direct transmission, reflection, and knife-edge diffraction to shed light on their interplay in the E-band regime.