M.S. - Electrical Engineering

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https://hdl.handle.net/10125/2047

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Now showing 1 - 10 of 149

EVALUATING DYNAMICS IN CONVERTER DOMINATED POWER SYSTEMS: AN RMS AND EMT SIMULATION APPROACH
(2024) Pramanik, Abrar Shahriar; Green, Daisy; Electrical Engineering
PROTON EXCHANGE MEMBRANE FUEL CELL MODIFICATION FOR CATALYTIC COGENERATION OF HYDROGEN PEROXIDE AND ELECTRICITY
(2024) Fernandez, Alexandra M.; St-Pierre, Jean; Electrical Engineering
Optical Jitter Metrology for Precision Pointing Satellites
(2024) Urasaki, Chase Masao; Zhu, Frances; Bottom, Michael; Electrical Engineering
DESIGN, FABRICATION, AND MEASUREMENT OF GENETIC PROGRAMMING DEVELOPED BROADBAND RF METAMATERIAL ABSORBERS
(2024) McDowell, Zion; Yun, Zhengqing; Electrical Engineering
Voltage Response Insights into Lithium-Ion Battery Diagnostic Techniques
(2023) Fernando, Alexa; Dubarry, Matthieu; Electrical Engineering
Methods for Extraction of Physiological Signals From Wrist-Worn PPG Sensor and Doppler Radar
(2022) Stankaitis, Grant; Boric-Lubecke, Olga; Electrical Engineering
Toward Assessment of Lung Water Content Using Wireless Cardio-Pulmonary Stethoscope Measurements
(University of Hawaii at Manoa, 2023) Leong, Christopher James; Iskander, Magdy F.; Electrical Engineering
Detecting abnormal excessive buildup of fluid in the lungs, or pulmonary edema, is crucial in preventing conditions such as heart failure, kidney failure, and acute respiratory distress syndrome (ARDS). Most existing methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. To provide continuous and non-invasive monitoring of lung water status, Hawaii Advanced Wireless Technologies Institute (HAWTI) invented the Cardio-Pulmonary Stethoscope (CPS), a low-cost device with chest patch radio frequency (RF) sensors that was proven to be able to detect heart rate, respiration rate, and changes in lung water content from a single RF measurement. The CPS measurement procedure and the accuracy of results have been verified in a National Institute of Health (NIH) sponsored clinical trial conducted in collaboration with The Queen’s Medical Center in Honolulu.This thesis presents recent advances in expanding the capability of the CPS for assessing lung water status, in addition to monitoring the change in lung water, using artificial intelligence (AI). An important first step in our AI pipeline is to build a database of a diverse patient population. To this end, we utilize an NIH dataset consisting of CT-scans of patients of various genders, ages, and body fat compositions. We then develop an automatic workflow that reads the CT-scans and creates 3-D models for high-fidelity simulation in Ansys High Frequency Structure Simulator (HFSS). From HFSS, we obtain scattering parameters (S-parameters) measured by the CPS at various lung water levels. Compared to data collection from clinical trials, this “Virtual Clinical Trial” approach is low-cost, less time-consuming, and risk-free. Using the database we built, we develop AI models which use the patient metadata, namely gender, age, fat thickness, and S-parameters from the CPS as input, and output its assessment of the lung water status (i.e., normal, edematous, and severely edematous statuses). For a cohort of over 200 diverse individuals, our AI models achieve above 70% accuracy in assessing the lung water status. Furthermore, our AI models are interpretable and simple to explain
A Novel Genetic Algorithm Based Method for Measuring Complex Permittivity of Dielectric, Lossy and Multilayered Materials with Thin Features Using Open-Ended Coaxial Probe
(University of Hawaii at Manoa, 2023) Zhang, Sunny Shuxuan; Iskander, Magdy F.; Electrical Engineering
The growing demands for novel smart and enabling metamaterial designs and semiconductor devices in health, energy, communication, and automatic industries have attracted researchers to develop designs of materials with “thin” features. For broadband complex permittivity measurements using open-ended coaxial probe (OECP), thin sample measurements present sig- nificant challenges since a large amount of power may go through the sample making the measured complex permittivity values unreliable. We developed a new approach for accurate measurement of thin material properties by back- ing the thin sample with a thick material of known complex permittivity. The process involves measuring the reflection coefficient of the layered unknown and known materials and using genetic algorithm (GA) to determine the complex permittivity in a broadband frequency range (can be from 200MHz to 20GHz) by comparing measurements with the simulated reflection coef- ficient of the same experimental arrangement using HFSS simulation. We have obtained complex permittivity results of multiple thin, dielectric, lossy, and multilayered materials in 1 to 10 GHz. Air gap between the OECP and thin material under test (MUT) can dominate the error terms in obtaining accurate measurement results, particularly for high permittivity and lossy materials. Some possible solutions to overcome the air gap problem, limi- tations of measuring high complex permittivity of materials, and minimum sample thickness are discussed.
Genetic Programming in Designing Advanced Metamaterial Absorbers
(University of Hawaii at Manoa, 2022) Chong, Edmond CM; Iskander, Magdy F.; Electrical Engineering
Metamaterials are artificial materials that possess properties otherwise not found in nature. The current state of metamaterial absorbers (MMA) in the lower gigahertz frequency (1-11 GHz) is sparse and commonly resides in the X-band (8-12 GHz). Typical 2D MMA have topologies designed by trial and error and are either compact with discrete operational frequencies or bulky and lossy to achieve broadband performance. Hybrid genetic programming (HGP) is proposed to create new compact design topologies in the lower gigahertz frequency with new material development. HGP can create new topologies optimized per input parameters, such as low frequency and high broadband absorptivity. These designs are built and simulated in Ansys High-Frequency Simulation Software (HFSS) and evaluated by HGP. Additional topologies, such as graphene and resistive sheet patterning, and resistive sheet insert, are explored and implemented with HGP to create compact, low-gigahertz frequency and high-absorptivity MMAs. The graphene-based and resistive sheet-based patterned designs achieved 80% bandwidth above 80% absorptivity from 4.6 to 11 GHz, up to 15 GHz, and from 3.83 to 9.13 GHz, respectively. Preliminary measurements of a fabricated resistive sheet insert design aligned with simulated results.
Identifying Consumer Drones Via Encrypted Traffic
(University of Hawaii at Manoa, 2022) Liang, David Vincent; Dong, Yingfei; Electrical Engineering
While consumer drones have been broadly adopted for many recreational applications, they have also become a low-cost and versatile tool for malicious activities. To address these threats, we need effective drone management and counter-drone measures. Identifying the concrete type of an invading drone is the crucial initial step. While most existing drone identification methods leverage radar, acoustic, or image processing, to the best of our knowledge, almost none of these investigate the unique communication patterns of drones for detection. In this thesis, we focus on the communication protocol between a drone and its controller and conduct an in-depth analysis of both encrypted and plaintext drone traffic. We propose a framework for identifying a specific type of drone among a known set of drones by analyzing its encrypted Wi-Fi communication traffic between a drone and its controller. The main idea of this approach is to utilize our understanding of drone communication details to match communication patterns in encrypted traffic to communication patterns in plaintext traffic. To explore the common cases on popular consumer drones, we select drones equipped with the most popular open-source drone control system, ArduPilot. Because the communications on these drones use the most popular communication protocol, MAVLink, we are able to conduct in-depth analysis of their plaintext communication traffic and identify patterns for our detection and classification. Collecting drone traffic and identifying concrete patterns in plaintext traffic is the first focus of this thesis. However, communication between a drone and its controller often is encrypted with a state-of-art protocol (802.11 WPA2 or WPA3). We will need a method to discover the communication patterns in such encrypted traffic and match them with patterns discovered in plaintext communication. This is the second focus of this thesis. In the first focus, we capture the encrypted traffic between our drones and their controllers, decrypt the traces, and analyze the corresponding plaintext traces to build a profile for each type of drone. We discovered that, as traffic in many control systems, the plaintext communications contain many messages with Unique and Non-Varying (UNV) sizes across multiple traces; such UNV messages also show strong periodical patterns, which make them ideal candidates for building traffic patterns. Furthermore, looking into the encryption protocols, we notice that 802.11 WPA2 (or WPA3) uses the AES-CCMP (Counter Mode CBC MAC Protocol) for encryption, which encrypts a plaintext into a ciphertext with a fixed 44-byte size increase. Using this fact, we can easily infer the plaintext message size based on the size of an encrypted message. Therefore, based on our analysis of both plaintext and ciphertext traffic, we have identified a set of UNV message sizes that helps us associate message patterns in the encrypted traffic with the message patterns in plaintext traffic. Specifically, we collect Wi-Fi traffic traces for three ArduPilot drones (3DR Solo, Intel Aero, and SkyViper Journey), and build their corresponding profiles. In the second focus, we propose two classification methods utilizing the drone profiles built in the first focus. To match the patterns in a target trace with the drone profiles, we first propose similarity-based methods to classify the target drone. Furthermore, we utilize well-known machine learning methods to compare the detected patterns in the target encrypted traffic with patterns in the drone class profiles. By utilizing our knowledge of the intricacies of the drone communication protocol, we are able to develop these unique methods which differ from existing approaches. We have conducted a concrete performance evaluation with our collected data to evaluate the proposed classification methods. Our results show that the similarity-based methods work well in many cases but also have clear limitations; the machine-learning-based methods have shown very high accuracy for all testing cases, proving the effectiveness of the proposed framework. In addition, we have implemented an existing method that uses the short-term statistics of encrypted traffic for detection. We compare the method with the proposed method with our data traces. The results show that the proposed framework has significant advantages over the existing method. It confirms that utilizing the details of both encrypted and plaintext drone traffic can further improve the performance of our method. In summary, we have proposed a drone classification framework based on our understanding of the unique characteristics of drone traffic, and the performance evaluation has shown the effectiveness of the proposed framework. In the meantime, there are several directions we like to explore to further improve the current methods and evaluation, e.g., collecting more traces under various flight patterns and modes, and expanding the proposed idea to other automated devices (e.g., self-driving cars).

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