Magdy F. Iskander

In this paper, we present an innovative approach for a simple, nondestructive and accurate method for broadband determination of complex permittivity of thin materials including those often used in patterning advanced metamaterial absorbers. While the measurement procedure involves using a simple coaxial probe method, the determination of complex permittivity values is based on an iterative procedure involving High Frequency Structure Simulation (HFSS) and a Genetic Algorithm (GA) based zero finding technique. The HFSS simulations include modeling the coaxial probe when in contact with the thin material under test and when backed by standard material and ground plane, and calculation of de-embedded reflection coefficient values, S11, at the probe and material interface. Through the developed GA-HFSS software interface, a GA fitness function was used to determine the complex permittivity values that best fit the measured S-parameters. Measurements were made on several standard materials including and obtained results were validated when compared with measurements using the shunt capacitance at the end of a coaxial line method. Results from the two measurements methods were within 1-2% difference in complex permittivity values in the frequency band from 500 MHz to 15 GHz, the compact radar band of interest. Measurement errors due to air gaps at the probe sample interface are analyzed to help guide measurements and achieve accurate results.

This paper investigates the interference issue between sensing and communication millimeter-wave (mmWave) active arrays, such as the sensing array (SA) and communication array (CA), which share the same time and frequency resources at a base station (BS) in integrated sensing and communication (ISAC) systems. To mitigate interference and improve signal quality at the communication user station (CUS), we propose a joint space-time interference cancellation method. This method integrates spatial interference suppression for SA-BS using a deep nulling weight design with 6-bit phase shifters and temporal pre-weight cancellation for CA-BS. Specifically, for the SA-BS array, deep nulling beamforming is achieved through exhaustive search (ES) or genetic algorithm (GA) techniques, leveraging an initial minimum mean square error (MMSE) weight with a 2-bit least significant bit (LSB) precision. For the CA-BS array, interference is further canceled by regenerating an accurate pre-weighted version of the interference signal, necessitating the channel estimation for both SA-BS and CA-BS arrays. Experimental results from a software-defined radio (SDR) platform equipped with developed dual mmWave active arrays confirm that the proposed space-time interference cancellation method effectively eliminates interference in over-the-air (OTA) environments, thereby enhancing the overall performance of mmWave active array systems practically.

In this communication, a 360° azimuth scanning array based on a compact reconfigurable element is proposed for aviation applications. In our design, a reconfigurable compact Yagi-Uda element is constructed by combing a driven element and two switchable parasitic elements. When constituting an array, more energy can be coupled to the parasitic elements and the current magnitude of the parasitic element reaches nearly the same as that of the driven element. Then, a tunable backside radiation zero point and high backside suppression can be realized for the Yagi-Uda element with only one reflector. The reconfigurable element is able to switch the radiation pattern between two broadside states in two directions and an endfire state. The measured results of the 1 × 8 prototype indicate that a wide scanning range from -180° to 180° is attained with a peak gain of 11.8 dBi and a gain fluctuation of less than 3 dB. The array has a small cross-section area of 0.47λ ₀ × 0.26λ ₀ . This work provides a compact reconfigurable antenna with full 360° coverage for aviation applications. The tunable radiation zero point has the potential to promote future anti-interference array designs.

Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’ volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict” approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch” RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases.

In this paper, a mechanically reconfigurable 1-D reflectarray actuated by a single motor is proposed. The elements are designed as metal tubes that are lifted by the ejectors. To achieve continuous phase growth in a limited space, the discontinuity of the Archimedean spiral is utilized. Then, a novel drive mechanism is constructed to transfer the power of the motor to each element. The combination of the Archimedean spiral cam and the follower effectively converts the rotation of the shafts to the linear motion of the element. In conjunction with the timing pulleys and timing belts, the Archimedean spiral cams generate a periodic changing phase gradient over time. The whole structure is supported by low-cost acrylic plates with fasteners. The main parts are composed of standard components and the overall structure is easy to realize. The reflectarray at normal incidence offers a beam steering range from -60° to 60° in one dimension at 5 GHz. The beam coverage test demonstrates a maximum improvement in the received signal level of 16 dB in the multipath environment. This work provides a new solution to the design of mechanically reconfigurable reflectarrays.

In 2020, the COVID-19 pandemic claimed 3 million lives worldwide in span of a year; the death toll is still on rise as of writing of this article. Hospitals around the globe overwhelmed with COVID-19 patients faced medical resource shortages preventing them from providing services to even severe cases, leaving patients to selfcare. The identified COVID-19 patients had to observe the symptoms escalation or take imaging tests such as CT scans to determine the disease progression. While these imaging methods provide detailed accounts of damage inflicted to lungs by COVID-19, they have their own limitations and risks. In this article, we use computer simulations to examine the possibility of using the Cardio-Pulmonary Stethoscope (CPS) to continually monitor the COVID-19 afflicted lungs. Using a CT scan of a real COVID-19 patient, an infection was introduced in the lungs of an anatomically correct digital human model to be studied using simulation method. The preliminary results of simulations showed that the least detectable size of infection was an ellipsoid of 0.9 cubic cm, and the CPS was most sensitive while detecting infection in the lungs without preexisting conditions like edema. Based on the results and resolution, signal sensitivity of the CPS to COVID-19 infection is established and it can be argued that CPS could be an alternative method for continuous monitoring of COVID-19 disease.

In this paper, we report the results of a field measurement campaign carried out inside a pedestrian tunnel at 24 GHz in two conditions, namely, empty tunnel scenario and busy tunnel scenario with pedestrian movement. The experiment measures the fading effects of various groups of pedestrian crowds using directional antennas at the transmitter and receiver for millimeter-wave radio communications. Having presented and analyzed the measurement data in several diverse scenarios, we have further investigated human scattering effects in the crowded pedestrian tunnel and performed ray-tracing simulation for an empty pedestrian tunnel condition. Because tunnel is an enveloped scenario that is not bound by any geographic areas, the results of this study can be applied to a wider scenario like other pedestrian tunnels across the globe. Above all, these findings contribute towards ensuring wireless connectivity for everyone even in a remote scenario like underground passages.

Prior Artificial Magnetic Conductor (AMC) designs using Genetic Programming (GP) showed that GP-based designs are competitive and often exceed those based on human expertise. However, the resultant typologies are of wild shapes and beyond intuitive explanation. To gain better physical insight into GP-designed AMCs, we develop equivalent circuit models for them and use Prony’s method to reveal their deep physical characteristics. Ansys Q3D Extractor is employed to help determine the RLC component values of the equivalent circuits. The natural resonant frequencies extracted by Prony’s method provide further explanation of the bandwidth of the structures. The equivalent circuit models and related natural frequencies of the GP designs can also provide guidance for human experts to reduce the optimization time and improve the performance of the designs.