Electrical and Computer Engineering Commons^™

An Approach Of Implementing Mtncl And Mtd3l Asynchronous Logic Paradigms On Fpgas, Kile T. Harvey

Electrical Engineering and Computer Science Undergraduate Honors Theses

This thesis describes the creation of component libraries for the implementation of Multi-Threshold NULL Convention Logic (MTNCL) and Multi-Threshold Dual-spacer Dual-rail Delay-insensitive Logic (MTD³L) on AMD 7 Series, AMD UltraScale, and AMD UltraScale+ FPGAs. The utilization of these libraries is identical to those used in the creation of MTNCL and MTD³L application-specific integrated circuits (ASICs), leading to intuitive use for designers familiar with the logic paradigms. Single-stage and pipelined designs were created using both libraries, which were then tested and verified to be logically equivalent to their ASIC counterparts. Future work will include creating and testing …

Construction And Characterization Of An Rf Anechoic Range, Logan Gentry

Electrical Engineering and Computer Science Undergraduate Honors Theses

Anechoic RF ranges are electromagnetically quiet spaces intended to provide conditions suitable for testing and classifying electronic equipment. Typically, ranges are constructed indoors, which presents a number of design challenges related to room geometry, cost, and material selection. This report describes the creation of an indoor anechoic range intended for characterization of antennas. Furthermore, the range is characterized for performance across a wide range of discrete frequencies in the UHF band according to CISPR standards, resulting in the determination of a lower frequency limit at which the room performance begins to degrade. The characterization of this range agrees with far-field …

Design Considerations Of A Gpu, Nicholas M. Devilliers

Electrical Engineering and Computer Science Undergraduate Honors Theses

With the current era of AI technology, the era of single instruction multiple data has become an increasingly viable solution to accelerate training. The problem is that while software to use GPUs and other hardware accelerators, designing GPUs and ASIC devices has become increasingly more expensive and there aren’t great examples of generic GPUs that anyone can use and modify. In this thesis, there are four design considerations that will be discussed and how they affect the result of a generic GPU. The four considerations that were talked about in the thesis are, word width, arithmetic type, number of stages, …

Finding Groundwater With Electricity: Research And Testing Of An Electro-Resistive Ground-Surveying Device For Use In Well-Drilling Applications, Samuel L. Heath

Senior Honors Theses

A custom-built electro-resistive water detection device is tested under various conditions to evaluate its overall performance and sensitivity to changing variables. The tests, designed to assess accuracy and precision, revealed that the prototype exhibits high reliability (~ 99%) for both Schlumberger and Wenner arrays across different electrode spacings and soil conditions. Moreover, the measured resistivity values from the soil tests aligned with established literature ranges for each soil type. The device also showed the ability to consistently detect changes in soil water content by producing measurable variations in resistivity. At a total cost of $181, this prototype can serve as …

Modeling Degradation In Li-Ion Batteries: Analyzing Single-Cell Degradation And Applying It To Customized Pack-Level, Mohannad Y. Alkhalil

Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research

The rapid expansion of lithium-ion (Li-ion) battery applications in areas such as electric vehicles (EVs), renewable energy storage, and portable electronics has drawn attention to the need for improving their performance, safety, and longevity. As Li-ion batteries become essential across technologies, understanding degradation mechanisms is critical for optimizing design and ensuring reliable operation. This work provides a detailed overview of modeling degradation in Li-ion batteries, focusing on single-cell behavior and its implications for pack-level performance.

The PyBaMM (Python Battery Mathematical Modeling) package, an open-source battery simulation tool written in Python, is used for single-cell simulations. Liionpack, another Python-based library, is …

Blockchain-Integrated Version Control For Secure And Transparent Software Supply Chains, Iwinosa W. Aideyan

All Theses

The software supply chain encompasses all stages of software development and delivery from initial coding and version control to integration and deployment. As development environments become increasingly distributed and reliant on external dependencies, ensuring the integrity, auditability, and consistency of code changes has become a pressing challenge. Traditional version control systems like Git, while effective for collaboration and tracking revisions, do not inherently provide tamper-evident commit histories. Features such as history rewriting (e.g., git rebase, git push --force) can be exploited to manipulate commit logs without detection, posing risks in security-sensitive domains. This thesis proposes a blockchain-integrated version control framework …

Design Of A Novel Low-Cost Hybrid V-Shape Spoke-Type Ferrite Ipm-Synrm For Ev Traction Using Fem Software, Kenghao Cai

Master's Theses

This paper presents a hybrid V-shape spoke type ferrite Interior Permanent Magnet Synchronous Reluctance Machine (PMA-SynRM) for electric vehicle traction application. Ferrite or ceramic IPM-SynRMs compared to Neodymium (NdFeB) based IPM-SynRMs typically have lower torque, power density, and efficiency overall. Motivated by rising costs and international policies making it increasingly more challenging to obtain rare-earth metals, alternatives should be explored. With the help of finite element method (FEM) software, Ansys Maxwell, this study probes into a novel three-phase ferrite-only hybrid Spoke-type V-shaped IPM-SynRM using Tesla Model 3's neodymium powered V-shape interior permanent magnet machine as a benchmark. The proposed motor …

A Heuristic Approach To Portrait Segmentation And Its Application To Synthetic Bokeh Generation, Charles J. Snead

Master's Theses

Segmentation of portrait images is an important technique used to separate the foreground and background of an image. This separation of layers is useful for selectively applying post-processing techniques to enhance the quality of the image, such as blurring the background. Automatic portrait segmentation is a complex process that can be completed with a high degree of accuracy using deep learning with neural networks, but training and inference are often very computationally expensive. This thesis aims to take a heuristic approach to portrait segmentation by combining classical image processing and computer vision techniques into a solution that can be run …

Enhanced Post-Capture Automatic White Balance For Srgb Images, Eric Huang

Master's Theses

Automatic white balancing (AWB) aims to correct color casts caused by varying illumination conditions, typically assuming access to RAW sensor data. However, many real-world applications involve only sRGB images that have already been processed by in-camera pipelines. In these cases, traditional AWB algorithms often underperform due to the nonlinear transformations done by these pipelines.

This thesis builds upon a data-driven color correction framework introduced by Afifi et al. that relies on RGB-UV histograms and learned color transforms. A revised automatic white balancing (AWB) framework that improves both color accuracy and runtime efficiency is proposed. A fallback routine is implemented to …

Advanced Day-Ahead Scheduling Of Hvac Demand Response Control Using Novel Strategy Of Q-Learning, Model Predictive Control, And Input Convex Neural Networks, Rahman Heidarykiany, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we present a Q-Learning optimization algorithm for smart home HVAC systems. The proposed algorithm combines new convex deep neural network models with model predictive control (MPC) techniques. More specifically, new input convex long short-term memory (ICLSTM) models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning (RL) algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems. As a novel RL approach, the proposed algorithm generates day-ahead HVAC demand response (DR) signals in smart homes that optimally reduce and/or shift peak energy usage, reduce electricity costs, …

Intelligent Crowdsourcing Based On Online Social Networks, Adedamola Adesokan

Electrical and Computer Engineering ETDs

The prevalence of Online Social Networks has completely changed how individuals communicate and cooperate. This dissertation explores the integration of intelligent crowdsourcing mechanisms into OSNs, focusing on game theory, reinforcement learn- ing, and social network dynamics to enhance the allocation of tasks, participation by users, and distribution of rewards. It proposes several models that utilize trust- based models, hedonic coalition games, and influencer dynamics for optimization of crowdsourcing. The major challenges to be addressed include task selection, reward distribution, and incentivizing participation, taking into consideration aspects re- lated to social influence, trust, and user engagement. The models so proposed have …

Grid Forming Inverters For Microgrid Operation Enhancement, Jan Westman

All Dissertations

Modern electric power systems are confronted with several major challenges including the increasing frequency and severity of weather events, aging infrastructure, high material costs, cyber-attacks, and substantial delays in global supply chains. Microgrids provide a solution to these challenges by insulating customers from the power outages occurring in their local distribution system. A microgrid is a small independent power system with its own power generation and energy storage resources that can supply customers’ electricity needs. When an outage occurs, a circuit breaker will disconnect the microgrid from the distribution system. Successful operation of the microgrid requires one or more devices …

Real-Time Active Thermal Management And Degradation Forecasting Of Power Electronics Building Blocks In All-Electric Ships, Ali Moghassemi

All Dissertations

With advancements in semiconductor technology, power electronic devices play a critical role in modern power systems, enabling efficient energy conversion in applications such as renewable energy integration, electric vehicle drives, industrial automation, and Navy ship power systems. However, the increasing use of power converters presents challenges such as harmonic reduction, cost optimization, reliability improvement, and thermal management, especially in weight- and space-constrained environments like wind turbines and shipboard systems. High-frequency switching, while reducing passive filter size, increases thermal stress on semiconductor devices, impacting efficiency and longevity. Active thermal control, which regulates junction temperature through power loss management, and degradation forecasting, …

Fairness And Robustness In Decentralized Federated Learning, Kaichuang Zhang

Theses and Dissertations

Federated Learning (FL) has emerged as a privacy-preserving paradigm that allows multiple clients to collaboratively train a machine learning model without sharing raw data. However, traditional FL relies on a central server for model aggregation, which introduces a single point of failure and makes the system vulnerable to server-side attacks or breakdowns. To address these limitations, Decentralized Federated Learning (DFL) has been proposed, eliminating the need for a central server and enhancing system resilience. Despite these advantages, DFL faces critical challenges related to fairness and robustness, especially under non-i.i.d. data distributions and adversarial conditions. In this thesis, we propose a …

Spin Coater Design With Pid Algorithm Using Polynomial Regression Approach And Bias Tuning For Tio2 Deposition Process, Geo Surya Andika, Nofrijon Sofyan, Donanta Dhaneswara, Akhmad Herman Yuwono

Journal of Materials Exploration and Findings

The thin-film deposition technique using spin coating offers a cost-effective alternative to Chemical Vapor Deposition (CVD) and Physical Vapor Deposition (PVD). The spin-coating process requires precise control of the motor drive system to ensure that the rotational speed, measured in rotations per minute (RPM), aligns with the set point and remains stable. This study presents the design and development of a spin coater prototype to achieve uniform thin-film deposition. The control method employed utilizes a Proportional-Integral-Derivative (PID) algorithm, incorporating a polynomial approach with bias tuning. The PID control was chosen to achieve stable operation in a non-linear system. The performance …

Empowering Optimal Operations With Renewable Energy Solutions For Grid Connected Merredin Wa Mining Sector, Md Ohirul Qays, Ravi Kumar, Minhaz Ahmed, Stefan Lachowicz, Uzma Amin

Research outputs 2022 to 2026

Mining sectors require a continuous and reliable power supply; however, reliance on traditional grid utilities results in high costs and disruptions and increases extreme carbon emission. The Merredin WA sector seeks to resolve critical energy challenges affecting mining operations in Western Australia. Thus, this research proposes an optimal solar PV system with battery storage and backup generation for the mining sector to ensure a stable and cost-effective power supply that reduces harmful environmental effect. A hybrid data-driven long short-term memory (LSTM)-classical optimization framework is designed here, thereby optimizing PV-battery storage operational cost savings and energy usage. The optimization results indicate …

Networked Multi-Agent Deep Reinforcement Learning Framework For The Provision Of Ancillary Services In Hybrid Power Plants, Muhammad Ikram, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Inverter-based resources (IBRs) are becoming more prominent due to the increasing penetration of renewable energy sources that reduce power system inertia, compromising power system stability and grid support services. At present, optimal coordination among generation technologies remains a significant challenge for frequency control services. This paper presents a novel networked multi-agent deep reinforcement learning (N—MADRL) scheme for optimal dispatch and frequency control services. First, we develop a model-free environment consisting of a photovoltaic (PV) plant, a wind plant (WP), and an energy storage system (ESS) plant. The proposed framework uses a combination of multi-agent actor-critic (MAAC) and soft actor-critic (SAC) …

Development Of A Portable Electroencephalogram With Display, Seth D. Davis

Electronic Theses and Dissertations

In this experiment, the goal was to design, build, and test an Electroencephalogram (EEG) circuit that would then be paired with a touchscreen display as a portable unit for use in Schizophrenic research at East Tennessee State University at the Quillen College of Medicine. The requirements for this prototype denoted that the device had to be able to obtain a signal from two electrodes placed on the head of an individual with a third attached to the ankle as a ground, amplify and filter the signal, and display the results on a touchscreen display for recording and analyzing. The circuit …

Towards Mitigation Of The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Jaime Merin Guzman

Open Access Theses & Dissertations

Detecting and localizing faults in communication networks is critical to maintaining reliable and efficient network operations. The Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm has demonstrated its potential to automate this task but suffers from significant performance degradation under low network load conditions, where limited network flow data reduces its ability to localize faults. This thesis proposes and evaluates the performance of a synthetic traffic generation algorithm to be used with NLOF:MLL. This algorithm strategically injects synthetic flows that supplement the insufficient real network flows, thereby improving NLOF:MLL's performance under low-load conditions. Specifically, we select network …

Generative Ai For 3d Printed Antenna Design, Jennifer Ann Chavez

Open Access Theses & Dissertations

This research explores the integration of generative artificial intelligence (AI) with a physics-informed particle swarm optimizer (PSO) to develop 3D printable microstrip patch antennas. A neural network was trained on a dataset of microstrip patch antenna geometries and their corresponding performance metrics: return loss and gain. The PSO used a fitness function prioritizing low return loss in potential antennas, eventually yielding novel antenna geometries with parasitic components. 3D printing constraints were also hard coded into the framework, thus preventing any geometries being generated that cannot be fabricated. When simulated using Ansys HFSS, the AI generated microstrip patch antennas exceeded the …

Development Of Si-Based Germanium-Tin Infrared Photodiode, Sylvester Amoah

Graduate Theses and Dissertations

Short-wave infrared (SWIR) and mid-wave infrared (MWIR) photodetectors have attracted increasing attention for the past few decades. SWIR and MWIR detectors are used for a wide range of applications in military and civilian defense, such as future automotive night vision, video surveillance security cameras, and integrated night vision in mobile/wearable electronics. The current market-dominating IR photodetector technology is mainly developed with the costly III-V or II-V material systems, such as InGaAs, InSb, and HgCdTe. While many efforts have been made towards the hybrid integration of III-Vs or II-VIs on a Si substrate, it is highly desirable to develop an alternative …

Hybrid Dc-Dc Converters For Soft Charging Capacitive Actuators: Modeling, Analysis And Design, Bahlakoana Mabetha

Dartmouth College Ph.D Dissertations

Recent trends in haptics, microrobotics and ultrasound technology have shown an increasing use of piezoelectric and other electrostatic actuators. These actuators are suited for miniaturized applications due to their high power density and favorable scalability at a small size. Their electrical impedance at lower frequency (their typical operating range) is capacitive, therefore, they can be modeled as capacitive loads.

The driving circuits for capacitive actuators need to deliver and recover (bidirectional) dominantly reactive power, unlike typical power electronics converters for resistive loads which deliver (unidirectional) real power to the load. Therefore, the circuits, operation, and optimization required to drive these …

Blind Construction Of A Party Check Matrix For Arbitrary Linear Block Codes, Aaron J. Womack

All Graduate Theses and Dissertations, Fall 2023 to Present

Communication systems are systems in which a transmitter sends data across a channel to a receiver. In any modern communication system, Forward Error Correction (FEC) is used to detect and correct errors that occur during transmission. As communication systems have developed over the years, many FEC algorithms have been proposed. Each FEC algorithm has parameters that can be selected based on the needs of the communication system. Typically, the transmitter and receiver are both aware of the FEC parameters, but in some cases the receiver may not know how the transmitter encoded its message. In this case, the receiver must …

Characterizing Human Mobility Patterns In Saudi Arabia Using Cellular Data, Meshal Alnefaie

Theses and Dissertations

The study analyzes human mobility in Saudi Arabia. Using crowd-source data, Riyadh mobility is analyzed to find trends and highlight mobility patterns of individuals in Riyadh. Then, the mobility of Riyadh is compared with that of Jeddah and Dammam in a similar data collection and analysis. Four mobility metrics are utilized: Number of Visited Locations (NLOC), Number of Unique Locations (NULOC), Radius of Gyration (RGYR), and Distance Traveled (DTRV). The results show interesting outcomes about individuals in the three cities. Although these cities are far from each other, they observe the same mobility patterns. These findings have the potential to …

Physics Embedded Neural Network: A Novel Data-Free Numerical Method For Solving Computational Physics Problems, Pawan Gaire

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A novel approach for solving partial differential equations (PDEs) using neural networks for scientific computing is introduced. The proposed approach, referred to as physics-embedded neural network (PENN), features a unique architecture that incorporates the PDE and boundary conditions information directly within the final fully-connected layer of the feed-forward neural network (NN). The key aspect of PENN is the parallel numerical embedding of a differential equation associated with physical problems within the activation function of the network’s final layer. This integration leads to a new class of computational solvers competitive with classical methods like the Finite Element Method (FEM) and capable …

Picosecond Laser Ranging At 1.5 Μm Using Dispersive Interferometry, Behzad Boroomandisorkhabi, Xiangrui Su, Mina Esmaeelpour

Electrical and Computer Engineering Faculty Research & Creative Works

Precise displacement measurement is essential for engineering, industrial, and scientific purposes. Ultrafast laser techniques are preferred for real-time applications due to their single-shot measurement capability and high resolution. To create a high-performance and cost-effective system with less complexity, capable of achieving real-time measurement with high micrometer spatial resolution, we have used a picosecond pulsed laser at the telecommunication wavelength of 1.5 μm in combination with dispersive interferometry. The instantaneous frequency measurement took place using the time-stretch technique incorporating dispersion compensating fiber induced chirp. Results using a 7-picosecond laser at 1.5 μm with a 10 MHz repetition rate are presented. Frequency …

Deep Learning In Lung Cancer Pre- And Post-Radiation Therapy: Diagnosis Of Malignancy And Radiation-Induced Lung Injury From 3d X-Ray Ct., Benjamin Peter Veasey

Electronic Theses and Dissertations

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection and accurate diagnosis being critical for improving patient outcomes. Additionally, the progression of Radiation-Induced Lung Injury (RILI) following Stereotactic Body Radiation Therapy (SBRT) for lung cancer presents a significant diagnostic challenge. This dissertation addresses these challenges by developing deep learning-based diagnostic tools for both pre-treatment lung nodule malignancy classification and post-treatment RILI identification using 3D X-ray CT imaging. The research is divided into two primary objectives. First, for lung nodule malignancy classification, we developed a biopsy-confirmed dataset, called NLSTx, to train and evaluate deep learning models while …

An Automatic Colorectal Polyps Detection Approach For Ct Colonography., Mohamed Yousuf

Electronic Theses and Dissertations

Colon cancer, also known as colorectal cancer, is a significant health concern, with increasing incidence rates, particularly among individuals under 50. This rise has led experts to recommend the introduction of regular screenings at 45 years of age for adults at average risk. Early detection through such screenings can identify precancerous polyps, allowing their removal before they develop into cancer. This proactive approach has the potential to reduce colorectal cancer deaths by up to 60%. In addition, research indicates that people diagnosed before age 50 have better survival rates, which emphasizes the importance of early diagnosis. Therefore, adhering to recommended …

Multimodal Emotion Recognition For Human-Robot Interaction Across Neuro-Diverse Populations., Ruchik Mishra

Electronic Theses and Dissertations

This dissertation explores the integration of multimodal data streams and artificial intelligence pipelines to understand human affect in neurotypical and children with Autism Spectrum Disorder (ASD). This dissertation captures human affect in the context of human-robot interaction. For this, multiple studies have been presented with both children with ASD and neurotypical adults. This dissertation makes four contributions: 1) The first study introduces autonomy during perspective-taking teaching sessions by making verbal content generation through large language models (LLMs). This system is the first of its kind for teaching perspective-taking in a semi-autonomous manner under the supervision of domain experts. Furthermore, this …

Robust Distributed Learning Against Both Distributional Shifts And Byzantine Attacks, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian

Electrical and Computer Engineering Faculty Publications

In distributed learning systems, robustness threat may arise from two major sources. On the one hand, due to distributional shifts between training data and test data, the trained model could exhibit poor out-of-sample performance. On the other hand, a portion of working nodes might be subject to Byzantine attacks, which could invalidate the learning result. In this article, we propose a new research direction that jointly considers distributional shifts and Byzantine attacks. We illuminate the major challenges in addressing these two issues simultaneously. Accordingly, we design a new algorithm that equips distributed learning with both distributional robustness and Byzantine robustness. …