Electrical and Computer Engineering Commons^™

Energy Efficient Spintronic Devices For Non-Volatile Memory And Hardware Ai, Walid Al Misba

Theses and Dissertations

Nanomagnetic devices have emerged as a promising alternative to conventional complementary metal-oxide-semiconductor (CMOS) devices due to their low energy dissipation and inherent non-volatility. However, the widespread adoption of these devices requires high-density, high-speed, reliable, scalable, and energy-efficient technologies. This thesis investigates the use of nanomagnetic memory devices as both conventional Boolean memory and multistate memory for hardware AI applications.

Magnetic tunnel junctions (MTJs) are nanomagnetic memory devices that can be switched reliably and energy-efficiently using stress-mediated switching. However, realistic material inhomogeneity and scalability pose challenges for stress-mediated switching of MTJs scaled to lateral dimensions below 50 nm. We demonstrate that …

Hybrid Design Approaches For Reconfigurable Em Structures, Jonathan D. Lundquist

Theses and Dissertations

The modern world is replete with reasons for employing reconfigurable electromagnetic (EM) structures, but some of the most pressing needs require advancements in power consumption, speed, and footprint [1]. Next generation air defense (NGAD) fighters could benefit from new electronic warfare (EW) countermeasures [2], as well as adaptive stealth technology [3], while doctors could benefit from reconfigurable microwave ablation antennas that can modify radiation pattern and input impedance to match changes in tissue during ablation and account for variations in tumor shapes [4]. The future of antennas, filters, and lenses is reconfigurable as real-world matters of life and death may …

Machine Learning Assisted Optimization For Calculation And Automated Tuning Of Antennas, Lauren Linkous

Theses and Dissertations

The Antenna Calculation and Autotuning Tool (AntennaCAT) software suite represents a significant advancement in the field of antenna design by automating the entire design, CAD, simulation, and optimization process compatible with several EM simulation software suites. It is the first comprehensive implementation of machine learning in this context. In particular, this work includes the capability to create and export structured datasets from the aforementioned EM software for iterative improvement and includes an expandable selection of optimizers.

Effect Of Annealing On Photoluminescence From Defects In Gan, Oleksandr Andrieiev

Theses and Dissertations

Annealing is a critical process in modern GaN technology, essential for achieving p-type conductivity by activating the Mg_Ga acceptor, as first demonstrated by Shuji Nakamura in the early 1990s. Despite the omnipresence of hydrogen as an impurity in GaN crystals, the precise mechanisms governing hydrogen diffusion and acceptor passivation remain only partially understood. The presented research investigates the effects of annealing-induced activation and hydrogen passivation on C and Be acceptors in GaN grown by MOCVD, HVPE, and MBE methods. We explored these effects by using several annealing techniques, gas compositions, and thermal regimes. A transient behavior between the C …

Bridging The Gap In Epsilon-Near-Zero Nonlinear Materials, Adam R. Ball

Theses and Dissertations

Studying how light and matter interact has been of interest to humans for thousands of years. From the invention of the first mirror, burning ants with a magnifying glass, to optical fiber communication, light has been central to mankind. Researchers have sought to take this to the extreme with nonlinear optics where intense laser light interacts with materials. The study of this thesis is to understand a new generation of materials that has come to light. Epsilon-near-zero (ENZ) materials have shown promise for ultrafast light manipulation in tandem with modern day CMOS compatible electronics. Here, I seek to bridge the …

Multi-Magnetic Material Transcranial Magnetic Stimulation Coils Development And Electric Field Measurement & Modeling Using Machine Learning, Mohannad Tashli

Theses and Dissertations

Transcranial Magnetic Stimulation (TMS) is a safe, effective, and non-invasive therapy for treating several psychiatric and neurological disorders. TMS is Food and Drug Administration (FDA) approved treatment and is commonly applied to patients who do not respond to medications for the treatment of clinical depression, smoking cessation, obsessive-compulsive disorder and migraine. Recently, there has been an increase in the development of electromagnetic neuromodulation techniques targeted at enhancing the effectiveness of TMS devices for the treatment of mental diseases. In TMS stimulation, focality is an important factor which determines the specificity of the pulses induced in different brain tissues. The electromagnetic …

External Runtime Execution Monitoring Of A Cyber Physical System Via Trace Interfaces, Peter Vaughan Truslow

Theses and Dissertations

In the past two decades, Unmanned Aerial Systems have progressed from expensive military hardware or one-off custom builds, to include off-the-shelf drones that can be purchased for a rather affordable price and flown by nearly anyone. As the technology and performance have improved, the door is opened to applications that require operation in environments where the consequences for failure are high, such as operating in the navigable airspace or in urban environments, or with human passengers. This requires a great deal of trust in the reliability and integrity of the control systems of the aircraft. A method of monitoring the …

Towards The Automation Of Post-Processing Of Additive Manufacturing Using Collaborative Robots, Logan Schorr

Theses and Dissertations

Despite the incredible potential that collaborative robots have, they are underutilized in several industries that could benefit greatly. These specialized fields, such as additive manufacturing, face challenges that can be solved with the use of robotics, including advanced computer vision, end-effector tooling, and process refinement. My research aims to utilize collaborative robots in an effort to solve the above stated problems, for the specific fields of additive manufacturing. Specifically within this field is the automation of post-processing; this is normally completely manual, as the tools for automation are not sufficiently developed to handle the flexibility required for additive manufacturing. This …

Functional Monitoring For Run-Time Assurance Of A Real-Time Cyber Physical System, Matthew W. Gelber

Theses and Dissertations

As cyber-physical systems (CPS) become more integrated into everyday life, the security of these systems must also be considered during their development due to their ever-increasing importance. With the growth of physical components in the system, more autonomous control requirements, and increased dependence on proper functionality, verifying system safety and correct operation becomes increasingly difficult. CPS have become more complex through the combination of additional hardware and the resulting interconnected software in many layers, each requiring unique security solutions. One example of such a safety-critical CPS embedded system is the Flight Control System (FCS) of an Unmanned Aerial System (UAS). …

Optimal Pilot Length For Key Generation In Correlated Wireless Channels, Danqi Li, Shihao Yan, Jia Zhang, Jiande Sun, Feng Shu

Research outputs 2022 to 2026

This work examines the optimal pilot length for key generation in correlated wireless channels. We first deduce the secret key capacity as a function of the pilot length N and other system parameters. Then, derive two fixed-point equations to identify the optimal N with and without an eavesdropper. Our analysis and numerical results confirm the existence of the optimal N that maximizes the secret key capacity, and both results for optimal N are consistent. Furthermore, the optimal N generally decreases with average signal-to-noise ratio and maximum Doppler shift. Our examination can provide useful guidelines in designing practical key generation systems.

Blind Source Separation And Denoising Of Underwater Acoustic Signals, Ruba Zaheer, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi

Research outputs 2022 to 2026

Due to the addition of new underwater vessels and other natural noise contributors, the underwater environment is becoming congested and noisy. Undersea monitoring sonobuoys receive multiple mixed acoustic signals from different vessels that need to be separated and identified in the presence of underwater noise (UWN). It is extremely challenging to separate highly correlated acoustic signals from a noisy mixture without prior knowledge of mixing process and propagation channel. Also, in many cases, the separated signals from the noisy mixture doesn’t accurately describe the correct signal. This study proposes a novel multi-stage method to separate underwater acoustic source signals from …

Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema, Amy Prendergast

Honors Theses and Capstones

Breast Cancer Related Lymphedema (BCRL) is a common co-morbidity in cancer survivors following neoadjuvant therapies such as chemotherapy, radiation, and/or surgery. It is brought about by the disruption in the lymphatic system (think lymph node biopsy) that leads to a buildup of lymphatic fluid in the arm. Current diagnostic strategies for this condition are merely retroactive, and fairly limited in the parameters that are examined to ensure patient well-being long term. We hypothesize that with an approach that mimics bioimpedance spectroscopy analysis, we will be able to provide a clinical support tool that would better determine early stages of lymphedema …

Disaggregating Longer-Term Trends From Seasonal Variations In Measured Pv System Performance, Chibuisi Chinasaokwu Okorieimoh, Brian Norton, Michael Conlon

Articles

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into …

Observability-Aware Path Planning For Autonomous Navigation, Raymond Brink Neistat

Open Access Master's Theses

Terrain-Aided Navigation (TAN) is a popular method of localization for GPS-denied vehicles, particularly in the marine domain. There are many ways to perform TAN in a marine setting, such as Bathymetric SLAM (BSLAM) and Bayesian Filtering with the aid of an a priori map. These techniques have been studied extensively, but show an overall lack of rigorous observability analyses. Without nonlinear observability analyses, TAN practitioners do not have an analytical indicator to know which areas of terrain will provide opportunities for the best localization performance. This thesis reviews current developments in the endeavors of nonlinear observability analyses as well as …

Non-Linearity Modeling And Quantifications For Practical Rf Interference Control, Shengxuan Xia

Doctoral Dissertations

"Radio frequency (RF) interference can degrade the receiving sensitivity of antennas (desense problem). It is essential to model the nonlinearity as it is the root-cause of the unwanted frequency components. Understanding the electromagnetic (EM) coupling or radiated emissions is also important.

Nonlinearity causes modulation-involved desense problems, and it consists of two categories: upconvertion of the baseband noise by the transmitting (TX) signal, and the passive intermodulation (PIM) of the transmitting signal itself. The upconvertion caused desense can be modeled and analyzed with the dipole-moment based coupling framework. PIM has been identified as another nonlinear distortion mechanism, specifically in the metallic …

Applications Of Computational Intelligence And Data Fusion Techniques For Biomedical Images, Anand Krishnadas Nambisan

Doctoral Dissertations

"The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall …

Grid Culture, Eliot Bates, Arseni Troitski

Publications and Research

In this essay, we provide our initial ethnographic research about lines, which at a superficial level can be considered a platform, but which is also synonymous with a community of 8800 users, and with 606k unique posts authored by these users that in aggregate constitute a discourse network (Aufschreibesysteme, see Kittler 1990). lines—as platform, community, and discourse network—is primarily designed to support the development, documentation and user-generated code efforts specific to a set of hardware synthesis-related objects (the two we will focus on are called Norns and Grid; we’ll discuss what they are and what they do through the subsequent …

Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri

Dissertations, Master's Theses and Master's Reports

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …

Application Of Fusion Based Deep Learning Models To Improve Millimeter Wave Beamforming, Abishek Subramanian

Dissertations, Master's Theses and Master's Reports

This study addresses the challenge of selecting millimeter Wave (mmWave) beamforming pairs for vehicle-to-infrastructure (V2I) communication, to mitigate latency in highly dynamic vehicular environments. We investigate the use of out-of-band sensor data as side information to model mmWave ray tracing paths and predicting a subset of top-K optimal beamforming pairs for efficient and low-latency searches. Unimodal-Fusion Deep Learning (F-DL) networks was applied to enhance mmWave beamforming process. We started by first investigating the centralized architecture, and then explored a novel distributed architecture through federated learning to minimize resource and latency overheads. The distributed architecture incorporates two biased client selection strategies: …

Optimizing Large Language Models And Multimodal Approaches For Biomedical Publication And Satellite Imagery, Youngsun Jang

Electronic Theses and Dissertations

This dissertation comprises two main sections: the first focuses on natural language processing (NLP) for extracting key information from scientific literature using large language models (LLMs), and the second addresses remote sensing for detecting natural disasters, such as floods, from satellite imagery using a multimodal approach. The first section investigates methods to enhance Transformer-based models in classifying and extracting information from biomedical scientific publications. Key contributions include the development of a custom dataset for classification and Question and Answering (Q&A) tasks, fine-tuning Transformer models like the Bidirectional Encoder Representations from Transformers (BERT) and addressing multi-span answer issues with the TAg-based …

Evaluating The Performance Of 5g Nr In Indoor Environments: An Experimental Study, Bikash Chandra Singh, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

The 5G wireless standard has emerged as a trans-formative technology with the potential to revolutionize various industries by providing enhanced connectivity and communication capabilities. This advanced standard offers a diverse range of applications, including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). In this Scientific research paper, we present a comprehensive analysis of the performance and capabilities of a deployed indoor 5G network in a controlled laboratory environment. The experimental setup comprises an Amarisoft Callbox, serving as the 5G core, along with a Remote Radio Head (RRH) and user equipment (UEs). Our primary objective …

Gas Assisted Electron Beam Patterning Processes, Deepak Kumar

Theses and Dissertations--Electrical and Computer Engineering

Radiolysis is a complex phenomenon in which molecules subjected to ionizing radiation form new chemical species. Electron-beam irradiation has proven to be a versatile approach for significantly altering materials’ properties and forms the basis for electron-beam lithography using both organic and inorganic resists. Electron-beam exposure is normally carried out under high vacuum conditions to reduce contamination and allow for unhindered interaction between the electrons and the resist material. Exposure under an ambient gas at sub-atmospheric pressures has been found to provide a distinct mechanism which can be exploited to circumvent some of the challenges associated with material processing and significantly …

Reinventing Integrated Photonic Devices And Circuits For High Performance Communication And Computing Applications, Venkata Sai Praneeth Karempudi

Theses and Dissertations--Electrical and Computer Engineering

The long-standing technological pillars for computing systems evolution, namely Moore's law and Von Neumann architecture, are breaking down under the pressure of meeting the capacity and energy efficiency demands of computing and communication architectures that are designed to process modern data-centric applications related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In response, both industry and academia have turned to 'more-than-Moore' technologies for realizing hardware architectures for communication and computing. Fortunately, Silicon Photonics (SiPh) has emerged as one highly promising ‘more-than-Moore’ technology. Recent progress has enabled SiPh-based interconnects to outperform traditional electrical interconnects, offering advantages like high bandwidth density, …

Design And Control Of Axial Flux Permanent Magnet Coreless Machines With Special Windings, Yaser Chulaee

Theses and Dissertations--Electrical and Computer Engineering

Permanent magnet synchronous machines (PMSMs), particularly those of the axial flux type, are being researched and developed for various applications such as HVAC systems, aviation propulsion, and electric vehicles. The coreless (air-cored) stator axial flux permanent magnet (AFPM) machine topology offers notable advantages over conventional designs by eliminating magnetic cores and their associated losses. These advantages include potentially higher efficiency, zero cogging torque, and reduced audible noise and vibration. Eliminating the magnetic core also allows for more effective cooling systems, as coolants can be in direct contact with the stator windings, potentially improving power density and specific torque.

The absence …

Ultra‐Fast Finite Element Analysis Of Coreless Axial Flux Permanent Magnet Synchronous Machines, Yaser Chulaee, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Large‐scale design optimisation techniques enable the design of high‐performance electric machines. Electromagnetic 3D finite element analysis (FEA) is typically employed in optimisation studies for accurate analysis of axial flux permanent magnet (AFPM) machines, which require extensive computational resources. To reduce the computational burden, a FEA‐based mathematical method relying on the geometric and magnetic symmetry of coreless AFPM machines is proposed to estimate the machine performance indicators using the least number of FEA solutions, thereby significantly lowering the running time. This method is generally applicable to AFPM machines with low saturation effects and cogging torque as exemplified for a printed circuit …

Design And Optimization Of High-Efficiency Coreless Pcb Stator Axial Flux Pm Machines With Minimal Eddy And Circulating Current Losses, Yaser Chulaee, Greg Heins, Ali Mohammadi, Mark Thiele, Dan M. Ionel, Ben Robinson

Electrical and Computer Engineering Graduate Research

This paper proposes a systematic multi-step design procedure for highly efficient printed circuit board (PCB) stator coreless axial flux permanent magnet (AFPM) machines with minimal eddy and circulating current losses. The process begins with initial sizing, providing specific coefficients based on experience with multiple design projects. It continues with the optimization of the machine envelope design using an evolutionary algorithm and computationally efficient 3D finite element analysis (FEA) models. The subsequent step focuses on the detailed design of a PCB stator, aiming to minimize eddy and circulating current losses. Several open circuit loss mitigation techniques are proposed based on analytical …

Design Optimization Of A Direct-Drive Wind Generator With A Reluctance Rotor And A Flux Intensifying Stator Using Different Pm Types, Ali Mohammadi, Oluwaseun A. Badewa, Yaser Chulaee, Donovin D. Lewis, Somasundaram Essakiappan, Madhav Manjrekar

Electrical and Computer Engineering Graduate Research

This paper presents a large-scale multi-objective design optimization for a direct-drive wind turbine generator concept that is based upon an experimentally validated computational model for a small-scale prototype motor of the same type. By integrating an outer reluctance-type rotor and a segmented stator with toroidally wound single-coil modules containing spoke-type PMs, the design optimization aims to minimize losses, active mass, and torque ripple while adhering to a power factor constraint. The AC windings and PMs are positioned in the stator and this concept enhances flux concentration, enabling the use of more affordable high energy non-rare-earth (special type) magnets. The exterior …

Large-Scale Design Optimization Of An Axial-Flux Vernier Machine With Dual Stator And Spoke Pm Rotor For Ev In-Wheel Traction, Ali Mohammadi, Yaser Chulaee, Aaron M. Cramer, Ion G. Boldea, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents the optimization study targeting a specific drive cycle for a MAGNUS-type axial-flux permanent magnet vernier machine (AFPMVM). The proposed MAGNUS machine has a novel design with a dual-stator configuration, where only one stator is wound, with a high-polarity spoke permanent magnet (PM) rotor. The machine topology has a 3D flux path, which necessitates the analysis of a large finite element (FE) model. However, due to the computational complexity and time required for such a large FE model, a new approach was developed. This approach involves a computationally efficient finite element analysis (CE-FEA) model combined with a single …

Fake News Detection In Online Platforms, Elena Shushkevich

Doctoral

This thesis presents research conducted during a Ph.D. program at Technological University Dublin from 2020 to 2024. The objective of this research is to develop and evaluate effective methods for detecting and classifying fake news in social media and press, addressing the critical issue of misinformation in the digital age. The relevance of this study is underscored by the increasing prevalence of fake news and its potential societal impact, emphasizing the importance of advanced tools for identifying and mitigating misinformation.

Economical And Environmental Evaluation Of Non-Residential Demand Response In The European Transition To Zero-Carbon Energy, Markus Fleshutz

Theses

To meet climate objectives, major energy consumers with local multi-energy systems (L- MESs) must transition to renewable energy sources soon. The variability of renewable energies requires increased operational flexibility for integration. In this context, demand response (DR), a strategy in which electricity consumers adjust their load profiles in response to incentives, has become crucial, offering cost-effective flexibility. It supports L-MESs in integrating renewable energy, reducing decarbonization costs, and enhancing resilience. However, quantifying the economic DR potentials for L-MESs under carbon emission constraints is complex, especially when considering investment options in distributed energy resources. This complexity hinders the rapid adoption of …