Doctor of Philosophy, The Ohio State University, 2024, Electrical and Computer Engineering

The central theme of this dissertation is to explore material interaction with electromagnetic waves in millimeter-wave (mmWave) and terahertz (THz) frequency bands spanning the range from 30 GHz to 3 THz. The implications of these interactions in the context of on-vehicle integration of mmWave automotive radars is discussed. Furthermore, specific mechanisms are exploited for broadband material characterization, and biomedical imaging. First, this research outlines the traditional broadband methods to characterize the electromagnetic properties of isotropic non-magnetic dielectric materials. In mmWave and THz regime, this data is not readily available in many cases. Utilizing established free-space techniques such as terahertz time-domain spectroscopy (THz-TDS) and quasi-optical transmission measurements, this research extracts this data for a diverse range of materials. In particular, we discuss the challenges related to the reliability of the permittivity extraction process in situations where the measurement may not have a high SNR across the bandwidth of interest. We circumvent this problem by cross-validating the data across multiple modalities to ensure consistency. Additionally, for thin dielectric films for which conventional methods fail, this research proposes a novel permittivity extraction technique from calibrated two-port S-parameter measurements of a coplanar waveguide. Interaction of mmWave radar signal with the near zone radome and bumper layers can impair the radar performance through reduction of signal-to-noise ratio and distortion of the pattern. Therefore, towards the goal of a `transparent' radome, the dissertation proposes a novel textured radome design aimed at optimizing transmission efficiency for mmWave automotive radar. Through a strategic optimization based on first-principles, this design exhibits an enhanced signal transmission throughout the entire automotive radar band of 76 – 81 GHz. The optimized design demonstrates an avera (open full item for complete abstract)

Committee: Niru Nahar (Advisor); Kubilay Sertel (Committee Member); Asimina Kiourti (Committee Member); Alebel Arage (Committee Member) Subjects: Electrical Engineering; Electromagnetics

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

This collection of works is an effort toward finding new solutions to challenges in electromagnetic devices, all connected by the implementation of vanadium dioxide (VO2). VO2 is a phase change material (PCM) that exhibits a reversible phase transition at 68 °C between monoclinic insulating and tetragonal conducting states. We designed, simulated, fabricated, and tested various devices in mmWave and optical wavelength domains. As an alternative to complex solid-state switching networks, we chose VO2 for its ability to react to various stimuli as a PCM, low phase transition temperature, and the freedom to design arbitrary geometries for reconfigurable and smart reactive devices. First, we experimented with the growth and characterization of VO2 thin films on sapphire and silicon substrates with Al2O3 buffer layers. Traditionally, VO2 has been deposited on sapphire substrates because of the lattice match between the two. This produces films with high resistivity contrast. However, sapphire is not as versatile a substrate material as silicon, being dielectric rather than semiconductor and extremely difficult to etch. To expand the realm of substrates useful for sputtering high quality VO2, we grew and compared such films on C-plane sapphire and silicon wafers with atomic layer deposited (ALD) alumina (Al2O3) films. Silicon has poor lattice match with VO2, and the alumina eliminates that interface. Furthermore, rapid thermal annealing (RTA) the alumina films before sputtering VO2 provides a basis for quasi-epitaxial films that have similar properties to those on the C-plane sapphire substrates. The figure of merit (FOM) resistivity contrast ratios for these variations are 9.76×104, 3.66×103, and 1.46×104 for C-plane sapphire, as- deposited amorphous ALD alumina on Si, and RTA ALD alumina on Si, respectively. We also characterized the films using X-Ray diffraction, atomic force microscopy, and scanning electron microscopy. In the next step, we examined the material (open full item for complete abstract)

Committee: Nima Ghalichechian (Advisor); Fernando Teixeira (Committee Member); Asimina Kiourti (Committee Co-Chair) Subjects: Electrical Engineering; Electromagnetics; Materials Science; Nanotechnology; Technology

Doctor of Philosophy, The Ohio State University, 2022, Electrical and Computer Engineering

Over the past few decades, the ubiquity of radio-frequency (RF) devices has improved connectivity and productivity in our lives through wireless communication and sensing technologies. To this end, vehicle-to-everything (V2X) communication and vehicular radar imaging technologies have become the key enablers of Intelligent Transportation Systems (ITS) to promote safety, automation, and coordination in traffic. To enable V2X communication, a limited amount of bandwidth in the 5.9 GHz spectrum is dedicated to vehicles for the exchange of basic safety messages with low latency. However, with the large-scale deployment of connected vehicles, the V2X-dedicated band faces the spectrum scarcity problem that lowers the reliability of vehicular communication. The scarcity of dedicated spectrum also limits the feasibility and capabilities of more advanced vehicular applications that rely on broadband communication. Besides, up to 4 GHz of contiguous bandwidth is allocated as the vehicular radar spectrum that is dedicated solely to vehicles in the 76-81 GHz millimeter-wave (mmWave) bands. To supplement V2X communication, the under-utilized vehicular radar spectrum can be leveraged by joint radar-communication (JRC) systems. The objective of JRC is to perform both data transmission and radar imaging using the same \textit{joint} waveform and transceiver hardware. In this dissertation, we investigate transmission optimization and scheduling approaches to enable vehicular JRC in mmWave bands using adaptive orthogonal frequency-division multiplexing (OFDM). First, we study the joint waveform design problem for wideband vehicular JRC. By exploiting the frequency-selectivity in wideband channels, we adaptively design subcarrier coefficients of OFDM to achieve long-range detection and communication performance. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is NP-hard. As an alternative to existing approaches, we propose time (open full item for complete abstract)

Committee: Eylem Ekici (Advisor); Ness Shroff (Committee Member); Can Emre Koksal (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2022, Engineering

Gait analysis measures the walking biomechanics and identifies the abnormality in regular walking patterns. This information is useful for clinical and rehabilitation purposes. The walking patterns can be observed using wearables, cameras, radars and Light Detection and Ranging (LiDAR). The LiDAR and cameras are expensive. Furthermore, cameras invade the privacy of a user. Wearables are beneficial in taking outdoor gait readings. But, they are cumbersome to wear for longer durations and have limited accuracy. The Millimeter Wave (MMW) radars have attracted significant attention in gait analysis because of their affordability, portability, simplicity, privacy and ability to operate in various ambient climate conditions. This work uses a low-cost MMW radar to develop a portable fall detection system using gait analysis. It examines the performance of popular Machine Learning (ML) techniques for gait analysis, including Support Vector Machine (SVM), Decision Tree (DT) and Neural Network (NN) for fall detection. The results indicate that NN achieves 99.79% training accuracy compared to 98.85% training accuracy for DT and 98.27% accuracy for SVM. The same trends are followed in testing accuracy. Therefore, the proposed fall detection system consists of MMW radar, NN-based Long Short-Term Memory (LSTM) and a low-cost NVIDIA Jetson nano-board, which shows promising results in terms of fall detection. We propose a novel solution, MMW radar system, for Human Activity Recognition (HAR). The mmGait combines micro-Doppler signatures of different activities and the skeleton pose estimation for 19 different joints. The proposed system uses a low-cost MMW radar, Kinect V2 sensor and Convolutional Neural Network (CNN) to classify five different activities. It can identify single or multiple activities in different environments. Furthermore, it can classify activities for different subjects in the same environment. The experimental results show that proposed system can classify (open full item for complete abstract)

Committee: Vamsy Chodavarapu (Advisor); Kurt Jackson (Committee Co-Chair); Youssef Raffoul (Committee Member); Amy Neidhard-Doll (Committee Member); Guru Subramanyam (Committee Member) Subjects: Electrical Engineering