Engineering Commons^™

Measured Phase History Data For Target Recognition Studies, Gregory A. Seltzer

Browse all Theses and Dissertations

Performing automatic target recognition (ATR) on full-size aircraft targets using inverse synthetic aperture radar (ISAR) data is challenging and expensive. The use of scale models and radar systems of such large targets saves time and reduces facility requirements. This study examines the feasibility of performing ATR on 1:144 scale model airplanes at Ka-band. The scale model and Ka-band radar simulate the collection of full-scale targets at VHF-band. The phase history measurement collections were completed in the Sensors and Signals Exploitation Laboratory (SSEL) at Wright State University. To ensure sufficient data for training and testing, the phase history data was augmented …

Application Of Multiple Data Augmentation Techniques To Improve Training With Synthetic Sar Data In Common Cnn, Stephanie M.V. Saich

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To address the issues of limited target data in the Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) problem set, synthetic data is often used to aid in filling the gap. This paper covers an in depth look at the use of colorization, dynamic range adjustment, and target extraction as data augmentation techniques to improve the accuracy of deep learning networks trained on synthetic SAR data. The use of multiple different data augmentations combine to dramatically improve the accuracy of a common Convolutional Neural Network (CNN) over the use of standard synthetic data. A comparison of increasing fraction of measured …

Electrochemical-Thermal Model Of A Lithium-Ion Battery, Paul Kalungi

Browse all Theses and Dissertations

Lithium-ion batteries are an integral component of energy storage systems for renewable energy applications owing to their high energy density. Extensive research has therefore been carried out, utilizing both experimental and computational methods, to aid in a deeper understanding of lithium-ion batteries. Challenges related to efficiency, safety and thermal management persist, particularly during high current draw, extreme temperature conditions and extreme dynamic current operation such as in electric vehicles. This thesis work presents an electrochemical-thermal model of a lithium-ion battery that simulates and analyzes the variation of electrical behavior, chemical behavior and thermal behavior. The electrochemical model is developed by …

Empirical Investigation Of Calibration Targets In Thz In The Near Field From 550 To 700 Ghz, Anais Kypris L. Rawson

Browse all Theses and Dissertations

The uncertainty of the standard calibration procedure for radar cross-section (RCS) measurement is studied for different targets measured in the near-field from 550 to 700 GHz. Using common calibration spheres and squat cylinders mounted on a styrofoam pedestal at waterline (zero-degrees elevation), the calibration difference measure is determined for each target. Similarly, the difference metric is determined for square trihedral and tophat targets placed on a ground plane and measured at different elevation angles. The mean calibration measure is calculated using the dual calibration target method and repeated measurements in an anechoic chamber. The specific THz system is described and …

Performance Degradation Of Gan Hemts Under Rf Aging: Implications For Wireless Communications Standards, Nathan Grant

Browse all Theses and Dissertations

This study examines the aging effects of GaN HEMTs, focusing on the CG2H40010 device under conditions that mimic the high-power, high-frequency environments of wireless communication systems. With the increasing adoption of GaN technology in RF applications, understanding its degradation mechanisms under CW stress and modulated signal characterization is essential for predicting device lifetime and ensuring performance standards for modern communication systems. RFALT was employed to stress the device using CW signals, while key performance metrics, such as gain compression, gate leakage, ACP, and EVM, were assessed using W-CDMA signals to replicate real-world dynamic stresses. The findings reveal that CW stress …

Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa

Dissertations, Master's Theses and Master's Reports

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …

Functional Verification Of Additively Manufactured Metallopolymer Structures For Structural Electronics Design, Nathan D. Singhal

Honors Undergraduate Theses

As an attempt to improve the overall cost-effectiveness and ease of structural electronics manufacturing, this study characterizes the mechanical and electrical responses of structures which are fabricated from a novel metallopolymer composite material by fused deposition modeling as they are subjected to quasi-static, uniaxial mechanical tension. Baseline values of tensile properties and electrical resistivity were first obtained via ASTM D638-22 standard testing procedures and linear sweep voltammetry (LSV), respectively. A hybrid procedure to measure in-situ mechanically dependent electrical behavior was subsequently developed and implemented. The mechanical and electromechanical testing was followed by the derivation of stochastic values for several mechanical …

Guarding The Grid: Exploring Iot And Iiot Security Vulnerabilities In Smart Power Systems, Nicole G. Parker

Honors Undergraduate Theses

The Internet of Things (IoT) encompasses the collective network of electrical devices and the technology that enables them to send and receive data. The use of IoT technologies in industrial settings, such as transportation, manufacturing, and energy is referred to as the Industrial Internet of Things (IIoT). With the expansion of IoT in homes and IIoT in the energy sector has come an increase in the number of devices connected with each other. Engineers have utilized this network to develop sophisticated smart systems that combine sensing, processing, actuation, and control to produce smart environments. Along with the benefits of IoT …

Machine-Learning-Assisted Design Of Deep Eutectic Solvents Based On Uncovered Hydrogen Bond Patterns, Usman Lame Abbas, Yuxuan Zhang, Joseph Tapia, Md Selim, Jin Chen, Jian Shi, Qing Shao

Markey Cancer Center Faculty Publications

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportuni- ties. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the …

Investigation Of Delta-Focused Ictal Electrical Source Imaging In Refractory Focal Epilepsy, Jared A. Rybarczyk

Theses and Dissertations--Electrical and Computer Engineering

Refractory focal epilepsy is characterized by the presence of seizures that cannot be controlled via anti-seizure medications. For patients suffering from this form of epilepsy, accurate identification of the seizure onset zone is a crucial step for many modalities of treatment. Electrical source imaging (ESI) allows for estimation of the seizure onset zone from electroencephalography. EEG feature extraction is an important step that can impact the final accuracy of source estimates. This work provides a review of 23 ictal ESI studies and proposes a delta-focused ictal ESI methodology. Our proposed delta-focused ictal ESI is implemented across 33 refractory focal epilepsy …

Electronics Design For Krepe Atmospheric Entry Capsule Avionics, Matt Ruffner

Theses and Dissertations--Electrical and Computer Engineering

This research documents the design, testing, and implementation of sensing, control, and communications subsystems for small atmospheric entry capsules. Atmospheric entry capsules pose a unique electronics design challenge given mission requirements, environmental constraints, and inherent risk involved in space based missions. Previous work on small satellites developed the CubeSat platform as a means of ensuring reliability after university-led small satellite missions experienced failures. The publication of the CubeSat platform allowed researchers to focus on the payload science utilizing a standardized form-factor and reusable electronics. Environmental and operational constraints of CubeSats and atmospheric entry capsules overlap and best-practices associated with building …

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

Mitigation Of Reflected Overvoltage In Wind-Turbine Generator-Converter Systems With A Smart Coil Concept, Lulu Wei

Theses and Dissertations--Electrical and Computer Engineering

In the recent decade, ultra-fast wide bandgap switching devices such as Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) have been increasingly utilized in power electronic converters for renewable energy power generation systems due to their advantages of enabling high energy efficiency and high power density. However, the much higher voltage slew rate (i.e., dv/dt) with SiC power converters may induce voltage reflection and reliability concerns in machine-converter systems, especially for medium-voltage systems with long cable connections. In wind-turbine power generation systems, generators are typically located in the nacelles, and medium-voltage power converters are mostly placed at the bottom of the …

Multi-Physics Modeling Of Immersion Cooled Electric Power Apparatuses, Reza Ilka

Theses and Dissertations--Electrical and Computer Engineering

Electric power conversion apparatuses such as electromagnetic transformers and power electronic converters play a backbone role in the global power and energy industries, such as power transmission and distribution, electrified transportation vehicle systems, industry automation, and the like. To investigate the performance (efficiency, reliability, etc.) of such critical power conversion apparatuses, high-fidelity multi-physics modeling becomes critical which involves dedicated coupling among multiple physical domains, including electrical, magnetic, thermal, mechanical, and material engineering. Nevertheless, the majority of the existing models for these power conversion apparatuses focus only on one or two physical domains, and the modeling fidelity needs to be further …

A Novel Processor Architecture Implementing The Stacked Error Diffusion Algorithm And Its Zynq-Based Realization, Qishi Hu

Theses and Dissertations--Electrical and Computer Engineering

Digital halftoning reproduces continuous-tone images using patterns of black and white dots, while multitoning extends this concept by incorporating inks with intermediate intensities. These techniques are extensively utilized in the printing industry to accommodate the limited range of inks available in printers. Stacked error diffusion is a high-quality multitoning algorithm that adheres to the blue-noise dithering standard. This thesis research studies the potential parallelism inherent in the algorithm and introduces the design of a novel processor architecture optimized for efficient execution. The architecture is realized on an FPGA development board featuring a Zynq SoC. Additionally, the hardware prototype can also …

Information-Theoretic Learning Framework Based On Covariance Operators On Reproducing Kernel Hilbert Spaces, Jhoan Keider Hoyos Osorio

Theses and Dissertations--Electrical and Computer Engineering

Information theory provides tools to quantify uncertainty, dependence, and similarity between probability distributions, which are crucial for addressing various machine-learning problems. However, estimating these quantities is challenging because data distributions are usually unknown, and only observations are available for analysis. In this dissertation, we advance the field of information-theoretic learning by developing a comprehensive framework using kernel methods for analyzing probability distributions using reproducing kernel Hilbert spaces (RKHS). By leveraging covariance operators in this representation space, we propose approaches to estimate a set of fundamental information-theoretic quantities, that, because of their resemblance with conventional quantities in information theory, we call …

Optimal Control And Evaluation Of Shipboard Power Systems, Musharrat Sabah

Theses and Dissertations--Electrical and Computer Engineering

Electric warships are involved in complex missions consisting of multiple simultaneous operations. In order to ensure mission success, future ships must be designed in a way that optimizes their performance in the presence of complex mission loads. The focus of this research is an effort to optimally control the performance of shipboard systems during missions involving multiple scenarios or vignettes. The evaluation of the performance of mission-oriented power systems is based on the degree to which these systems deliver power to the loads required to perform the mission at hand. The performance of such systems involves a dynamic interplay between …

High Reliability Motor-Drive Systems For Electric Aircraft Propulsion, Majid Tahmasbi Fard

Theses and Dissertations--Electrical and Computer Engineering

The transition towards electric propulsion in aircraft applications requires significant advancements in enhancing the efficiency and power density of power transmission and distribution systems. Among the critical power components in electric aircraft propulsion systems, power electronic converters hold paramount importance as they provide a pivotal interface between batteries and propulsion motors while providing a high degree of controllability and flexibility to the system. Integrating medium voltage DC (MVDC) and the emerging wide bandgap switching devices into power converters not only amplifies converter performance but also unlocks multifarious system-level benefits, including reduced power losses, weight savings, and the utilization of high-speed …

Experimental Investigation Of Self-Mixing Interferometric Technique For Displacement Assessment, Dulanjana Anuhas Egodagamage

Chulalongkorn University Theses and Dissertations (Chula ETD)

The self-mixing interferometric (SMI) technique has proven effective for achieving precise, contactless measurements of submicron displacements. This study aims to further investigate and experimentally assess the capabilities of the SMI technique in displacement sensing. While extensive research has been conducted on the SMI technique for displacement sensing, its performance can still be further explored in scenarios where the target exhibits varying displacement amplitudes and oscillation frequencies. Understanding the limitations and ranges of the SMI technique is crucial for advancing laboratory-based setups into practical applications. An experimental optoelectronic setup was used in this work, employing a semiconductor laser diode module integrated …

การพัฒนาแพลตฟอร์มเมตาเวิร์สแบบเว็บที่มีระบบสนับสนุนปัญญาประดิษฐ์, สืบพงศ์ น้อยศรี

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์ฉบับนี้นำเสนอการพัฒนาต้นแบบแพลตฟอร์มเมตาเวิร์สที่มีขีดความสามารถขั้นพื้นฐานครอบคลุมระบบอวาทาร์ ระบบผู้ใช้หลายราย ระบบสื่อสารตามเวลา ระบบการเชื่อมต่อแชตบอต และฉากโลกเสมือนจริงสามมิติที่เหมือนจริง แพลตฟอร์มที่พัฒนาขึ้นนี้ถูกนำไปใช้ประโยชน์กับหน่วยงานภาคการศึกษาและภาคธุรกิจ ทั้งหน่วยงานภายในจุฬาลงกรณ์มหาวิทยาลัยและสถาบันการศึกษาทั้งในประเทศและระดับนานาชาติ ผลการทดสอบพบว่าระบบทำงานได้ให้ผลสัมฤทธิ์ที่ดีและมีคุณภาพตามความต้องการใช้งานในทางปฏิบัติ สามารถประยุกต์ใช้กับการสร้างห้องเรียนเสมือนออนไลน์ในโลกเสมือนจริง รองรับระบบการสื่อสารระหว่างผู้ใช้ทั้งภาพ เสียง และข้อความ อีกทั้งยังสามารถใช้งานร่วมกับระบบแชตบอตจากภายนอกได้ผ่านระบบเอพีไอที่พัฒนาขึ้น ผลการทดสอบบนระบบคลาวด์ของบริษัท โทรคมนาคมแห่งชาติ จำกัด แสดงให้เห็นว่าระบบสามารถรองรับปริมาณผู้ใช้งานได้ไม่น้อยกว่า 30 รายพร้อมกัน ผลการทดสอบในเชิงประสบการณ์การใช้งานมีระดับความพึงพอใจในเชิงบวก งานวิจัยและพัฒนาจึงให้ประโยชน์และเป็นจุดเริ่มต้นของการประยุกต์ใช้เทคโนโลยีเมตาเวิร์สในวงกว้าง

Blockchain Based Framework For Secure And Decentralized Energy Trading - Transforming Local Energy Markets For Sustainable Communities, Saurabh Sachdeva

Dissertations and Theses

The emergence of prosumers provides an opportunity for the setup of a local energy market (LEM) where individual households with distributed energy resources (DERs) can produce, store, and trade energy. Peer-to-peer(P2P) and decentralized energy trading (ET) can be implemented between the participants within or across microgrids. Several solutions based on the existing technologies have been proposed worldwide for the integration of prosumers in the existing energy setup and to enable and support ET, but these solutions present the issues of centralization, data integrity, and confidentiality, user and anonymity, and transparency. The applications of blockchain technology have recently become fascinating for …

Ring Resonators Intergrating With Dichoric Materials And In Spin-Valley Controlled Photonic Topological System, Yuma Kawaguchi

Dissertations and Theses

Photonic technology plays an important role in the development of diverse applications, ranging from optical telecommunications to sensing and imaging. The utilization of photonic circuits emerges as a promising platform for the establishment of advanced communication systems characterized by high speed and large capacity. This advancement is crucial to facilitate fast and efficient data transfer while concurrently managing multiple devices. It is imperative that essential components such as lasers, modulators, and isolators exhibit compactness without incurring significant losses.

Non-reciprocal devices represent a valuable addition to photonic circuits, enabling the creation of unidirectional waveguides that function as optical isolators, preventing signals …

Hyperspectral And Polarimetric Imaging Of The Ocean For The Characterization Of The Surface Effects And Measurement Uncertainties, Mateusz Malinowski

Dissertations and Theses

Ocean and coastal waters are monitored by Ocean Color satellite sensors to determine concentrations of chlorophyll and water properties and identify areas of algal blooms and other events. The light radiance from the ocean is weak in comparison with the sky radiance, which requires very accurate atmospheric correction of the radiance measured at the top of the atmosphere (TOA) on the satellite and heavy validation of the derived water leaving radiance by field measurements from the ships and ocean platforms. For TOA and especially above water radiance the skylight reflected from the ocean surface represents one of the main sources …

Re-Envisioning Electric Railway Power Systems In Dense Urban Regions, Rohama Ahmad

Dissertations and Theses

Grid decarbonization and modernization are key requisites to combat global warming. Decarbonization entails the deployment of renewable energy sources, improving the energy efficiency of major consumers, and the electrification of the transportation and building sectors. Various US states and cities have pledged to reduce greenhouse gas (GHG) emissions (e.g., the Climate Leadership and Community Protection Act (CLCPA) in New York aims to reduce emissions to 40% below 1990 levels by 2030 and then to 85% below 1990 levels by 2050) via a set of targets in terms of renewable energy, energy storage system (ESS) capacity, and the number of electric …

Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall

Civil & Environmental Engineering Faculty Publications

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …

Information Access For Infrastructurally-Challenged Environments And Beyond Through Mutually Aware Spectrum Sharing Technologies, Karyn Doke

Electronic Theses & Dissertations (2024 - present)

The Radio Frequency (RF) spectrum is scarce and to make it available for new mobile wireless services, regulators are forced to re-allocate spectrum from existing services or develop mechanisms to share spectrum with new entries. Television White Space (TVWS) and Citizen Broadband Radio Service (CBRS) are two examples of recently commercialized spectrum sharing technologies. TVWS enables sharing among fixed wireless broadband technologies (secondary users) and terrestrial TV broadcast services (primary users). CBRS enables spectrum sharing among 5G/LTE (secondary users) and naval radar (primary users). With both technologies, a central database determines when it is safe for secondary users to operate …

Sparse Representation Learning For Temporal Networks, Maxwell Mcneil

Electronic Theses & Dissertations (2024 - present)

Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …

Anomaly Detection Approaches Of Energy Storage Systems Using Kalman Filter And Machine Learning Techniques, Phadungsak Tubuntoeng

UNF Graduate Theses and Dissertations

The increasing prevalence of cyber-attacks poses a significant concern, particularly within critical infrastructures like the power system. Such attacks have the potential to cause substantial impacts on essential services, economic stability, and national security. Energy storage systems (ESS) are integral components of the power grid and are particularly vulnerable to cyber threats. These vulnerabilities can be exploited through various means, including two-way communication, web portals, and remote access.

Given the critical nature of ESS, the ability to detect and mitigate malicious cyber-attacks is imperative. Various methods can be employed for cyber-attack detection, including signature-based, anomaly-based, and behavior-based approaches. In this …

Integrating Therapy Into Play: Stand-On Ride-On For A Child With Cerebral Palsy, Kira Flanagan

UNF Graduate Theses and Dissertations

This study focuses on the development of an adaptive ride-on toy specifically designed for a 2.5-year-old child with spastic diplegic cerebral palsy. As a feasibility study, the primary objective of this innovative device is to enhance motor function, foster autonomy, and improve the overall quality of life for the child. The Power Mobility Device (PMD) integrates actuation and steering modifications, and a harness mechanism tailored to meet the child's unique needs. Comprehensive assessments of the child's spatiotemporal gait characteristics were conducted before and after a three-month usage period. The results reveal significant improvements in the child's ability to control dynamic …

Evaluating The Performance Of Egemaps Features In Depression Detection Using E-Daic Subsets, Joshua Turnipseed

Graduate Research Theses & Dissertations

This paper investigates the performance of the eGeMAPS (extended Geneva Minimalistic Acoustic Parameter Set) feature set in detecting depression from audio samples using subsets of the E-DAIC (Extended Distress Analysis Interview Corpus) database. With depression affecting a significant portion of the U.S. adult population, efficient detection methods are critical for timely diagnosis and treatment. Various classifiers in the WEKA machine learning toolbox are used to evaluate the performance of eGeMAPS features in distinguishing between depressed and nondepressed (D&ND) individuals. Our methodology involves creating balanced subsets of E-DAIC, extracting eGeMAPS features using openSMILE, and testing different machine learning models. This study …