Engineering Commons^™

Sensitization Of Europium Oxide Nanoparticles Enhances Signal-To- Noise Over Autofluorescence With Time-Gated Luminescence Detection, Hunter Miller, Jessica Q. Wallace, Hui Li, Xing-Zhong Li, Ana De Bettencourt-Dias, Forrest Kievit

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Clinical translation of nanoparticle-based therapeutics has been limited, and a lack of preclinical delivery characterization is partly to blame, limiting our understanding of the mechanisms of failure. The improvement of the preclinical delivery assessment requires nanoparticles with higher detectability. This work focused on the exploration of several aromatic carboxylic ligands (terephthalic acid, quinaldic acid, and kynurenic acid) for the sensitization of europium oxide nanoparticles with a long emission lifetime to overcome cellular autofluorescence, a key confounder of detection in luminescence-based bioimaging. A facile one-pot synthesis and ligand exchange process generated and sensitized ultrasmall Eu₂O₃ cores. As reflected …

Performance Evaluation Of Databases For Packet Capture And Analysis, James Vong, Daniel Kareti

Computer Science and Engineering Senior Theses

Today, the presence of smart devices is constantly on the rise, especially for Internet of Things (IoT) devices. However, their utility-based design means that these devices are limited in computing power. Due to this limited computing power, the devices are more prone to cyber-security threats, and it is vital to construct a method to aid in network traffic analysis, bolstering defense mechanisms to thwart any malicious attacks. To analyze their network data efficiently and externally, we constructed a test-bed infrastructure to measure and evaluate the performance of databases. In this thesis, we have chosen to compare two databases which are …

Scdi: Privacy-Preserving Device Identification, Ethan Shenassa, Michael Castillo

Computer Science and Engineering Senior Theses

As internet security concerns grow due to the increasing complexity of cyberattacks, heterogeneity of network-connected devices, and popularity of insecure Internet of Things (IoT) devices, network administrators require more intelligent ways of managing and securing their networks. One crucial step in this regard is the ability to identify and classify devices from the edge of networks. However, many recent advances in network security come at the cost of user privacy, as device classification algorithms often require packet payload data, Internet Protocol (IP) addresses, or other sensitive information. This paper proposes the Shenassa-Castillo Device Identifier (SCDI): a software network device identifier …

Technologies For Wearable Seizure Detection: A Systematic Review, Rhema Losli

University Honors Theses

Knowing when a seizure occurred is helpful because this information can be used to evaluate the effectiveness of seizure interventions and possibly alert caregivers to emergency situations. The current practice for recording seizures outside of a hospital and without sensors is through keeping a self-reported seizure diary. This practice may be unreliable if the diary is not updated or the person having the seizure does not realize it is happening. Wearable seizure detectors aim to solve this problem by reliably recording when a seizure happened and either sending out an alert or storing the data for later analysis. In this …

Preservation Of Biomass In Underground Capsules Using An Open-Source Wireless Water Activity Sensor System: Capstone Review, Joshua Varughese

University Honors Theses

This paper highlights the progression of a two-term senior capstone project in the ECE department at Portland State University. Sponsored by Dr. David Burnett from PSU's WEST Lab, the project aims to produce an affordable wireless monitoring system for water activity. The example scenario focuses on carbon sequestration where wood biomass is buried underground in enclosed capsules. For optimal sequestration, microorganisms capable of decaying the wood and releasing carbon back into the environment must be eliminated. Water activity, a key metric for measuring microbial activity, must be below 0.61 to prevent microbial survival. The system this project was designed for …

Ai-Based Hazard Detection For Railway Crossings, Darren Espinoza, Gasser G. Ali, Constantine Tarawneh

Mechanical Engineering Faculty Publications

Grade crossings are critical elements of the railway infrastructure due to the potential risk of vehicle collisions with trains. According to the National Highway Traffic Safety Administration, there were more than 1,600 vehicle-train, and 500 human-train collisions in 2020. Researchers, transportation organizations, and government bodies are constantly exploring practices and technologies to improve safety at crossings. Examples of safety standards include sensors, motion detectors, depth cameras, and many other innovative technologies. The goal of this paper is to investigate the applications of computer vision using Artificial Intelligence (AI) deep learning models to enhance railway safety. Deep learning models can provide …

Kernel Ridge Regression In Predicting Railway Crossing Accidents, Ethan Villalobos, Constantine Tarawneh, Jia Chen, Evangelos E. Papalexakis, Ping Xu

Mechanical Engineering Faculty Publications

Expanding on the insights from our initial investigation into railway accident patterns, this paper delves deeper into the predictive capabilities of machine learning to forecast potential accident trends in railway crossings. Focusing on critical factors such as “Highway User Position” and “Equipment Involved,” we integrate Kernel Ridge Regression (KRR) models tailored to distinct clusters, as well as a global model for the entire dataset. These models, trained on historical data, discern patterns and correlations that might elude traditional statistical methods. Our findings are compelling: certain clusters, despite limited data points, showcase remarkably Root Mean Squared Error (RMSE) values between predictions …

Spectral Clustering In Railway Crossing Accidents Analysis, Ethan Villalobos, Hector Lugo Iii, Biqian Cheng, Miguel Gutierrez, Constantine Tarawneh, Ping Xu, Jia Chen, Evangelos E. Papalexakis

Mechanical Engineering Faculty Publications

This study employs graph mining and spectral clustering to analyze patterns in railway crossing accidents, utilizing a comprehensive dataset from the US Department of Transportation. By constructing a graph of implicit relationships between railway companies based on shared accident localities, we apply spectral clustering to identify distinct clusters of companies with similar accident patterns. This offers nuanced insight into the underlying structure of these incidents. Our results indicate that “Highway User Position” and “Equipment Involved” play pivotal roles in accident clustering, while temporal elements like “Date” and “Time” exert a diminished impact. This research not only sheds light on potential …

Development Of Rail Anchor Testing Through Literature Review Of Cwr Buckling Resistance Evaluation, Juan Rodriguez, Siang Zhou, Constantine Tarawneh, Alberto Sanchez, Teresa Salazar-Flores, S. Mustapha Rahmaninezhad, Hameem Gorabi

Mechanical Engineering Faculty Publications

Continuously welded rail (CWR) is among the most used railroad systems worldwide with great improvements compared to jointed tracks, including refined ride quality, increased fatigue life of track and rolling stock, and reduced maintenance costs. Rail buckling is one of the key remaining issues for CWRs to further reduce safety hazards and infrastructure deterioration, and save required track retrofit material and efforts. CWR buckling is induced by combined longitudinal, lateral, and torsional forces on the track that are caused by the synergy of rail components, the loading from moving trains, the interaction between track and substructure, and the effect of …

Ai-Enabled Vibration Sensing System For Early Detection Of Trains At Active Highway-Rail Grade Crossings, Mohsen Amjadian, Md. Masnun Rahman, Constantine Tarawneh, Valik Villarreal, Dylan Rocha

Civil Engineering Faculty Publications

Highway-rail grade crossings (HRGCs) play an essential role in ensuring the secure traversal of road users across railway tracks. However, despite their significance, they present safety challenges, particularly when trains go undetected, heightening the risk of potential collisions between the road user and train. This paper aims to explore the viability of employing vibration sensors for detection and characterization of an approaching train’s speed at HRGCs. The methodology involves analyzing rail vibrations and developing a time series predictive machine learning (ML) model. To accomplish this, a Finite Element (FE) model of a ballasted track railway is created in SAP2000, consisting …

Use Of Nanoparticle Additives To Achieve Desirable Properties In Fluorine-Free Firefighting Foams, Annelise Kathleen Curtin

USF Tampa Graduate Theses and Dissertations

Class B Firefighting foams play a crucial role in protecting equipment and people fromliquid fuel fires but concerns regarding the impacts of per- and poly-fluoroalkyl substances (PFAS) have resulted in bans on the use of aqueous film forming foam (AFFF). Fluorine-free replacements are yet to achieve the same level of performance. Nanoparticles including silica and clay have been used to improve foam stability, especially in environments where hydrocarbon fuels are present. These materials have also been used in solid composites to improve fire resistance. The stability of aqueous foams containing silica and Laponite RD nanoparticles along with surfactants of different …

Modified Method For Assessing The Required Expenditures And Estimated Time Of Hard Coal Mine Liquidation, Andrzej Chmiela, Małgorzata Wysocka, Adam Smoliński

Journal of Sustainable Mining

The restructuring of hard coal mining requires significant budgetary expenditures. A comprehensive scientific approach may facilitate the rationalisation and minimisation of mine closure costs. This study proposes a method for the preliminary estimation of the costs and time required for the potential liquidation of a hard coal mine. In addition to a literature review, a statistical analysis and a case study, personal interviews were conducted with individuals with direct management over the restructuring, reclamation and liquidation processes pertaining to mines undergoing closure.

The assessment method is based on an analysis of the mine liquidation costs, divided into successive years of …

Deep Learning Based Single Image Super-Resolution, Samuel Smith

Computer Science and Engineering Senior Theses

Single image super-resolution (SR) involves taking a given low-resolution (LR) image and generating a corresponding high-resolution (HR) image. This is a core task in the computer vision field due to its multitude of applications ranging from helping current-day issues of storage and transfer of data to restoration of low-resolution images. The current strategies, however, struggle to reach the quality needed for their widespread use and are often too resource-intensive for the average consumer. While other lightweight SR techniques exist with different techniques, like Cascading Residual Networks [3], their success comes at the cost of expensive technology inaccessible to most situations. …

Distant Horizon: Exploring Human-Ai Interaction Through Video Games, Gabe Labadie, Dalia Suszko, Max White

Computer Science and Engineering Senior Theses

As the field of Artificial Intelligence (AI) continues to grow and become more and more integrated into our everyday lives, it has begun to raise ethical concerns surrounding authority, autonomy, and responsibility. Researchers and consumers alike have begun to wonder how much trust we are willing to place in a non-human decision maker, especially if an Artificial General Intelligence (AGI) capable of surpassing human cognitive capabilities is ever developed. If we are willing to let an AI write essays for us, are we willing to let it manage a business? A government agency? What about a nuclear reactor?

Our project …

Daily Digest: A News Aggregation Site, Justin Wang, Jack Maguin, George Orloff, Justin Groves

Computer Science and Engineering Senior Theses

A staggering amount of news articles are uploaded every day – approximately 5,000 in the United States alone [1]. That volume of information causes difficulty for many people who try to stay up-to-date with current events. The number of articles and the multitude of sources that they come from can feel overwhelming. In our project, we attempt to tackle this issue. We use web scraping to collect a dataset of news articles and combine it with a Large Language Model (LLM) capable of processing those articles to generate news summaries for the user. The user interacts with the program through …

General Purpose Tuning Data Visualization, Chris Augustine, Francisco Salinas, Aakash Shetty

Computer Science and Engineering Senior Theses

This project centers on the visualization of High-Performance Computing (HPC) data obtained from the GPTune website. GPTune serves as a valuable resource for HPC experiments, providing a wealth of performance data from tuning studies and optimization tasks. Our objective is to develop an advanced data visualization framework tailored to GPTune’s datasets. Utilizing state-of-the-art visualization techniques, we aim to create an interactive platform that allows users to explore, analyze, and derive insights from the diverse tuning experiments conducted on HPC systems. The visualization tool will facilitate the identification of optimal configurations, performance trends, and patterns within GPTune data, empowering researchers and …

Applied Auto-Tuning On Lora Hyperparameters, Darren Inouye, Lucas Lindo, Robin Lee, Edmund Allen

Computer Science and Engineering Senior Theses

This senior design project explores the application of Bayesian optimization-based auto-tuning techniques on the low-rank adaptation (LoRA) fine-tuning of large language models (LLMs), demonstrating how fine-tuning methods can reduce training times and costs, albeit with a slight trade-off in accuracy. However, little is known about the optimal hyperparameters on LoRA and its variants for those methods. This project addresses this lack of knowledge by analyzing data gathered from auto-tuning LoRA hyperparameters to determine the most optimal parameter configurations for a model’s accuracy and training efficiency.

The team has implemented a pipeline utilizing many different technologies. The main technology driving the …

9-Axis Motion Tracking To Aid Therapeutic Recovery Via Visualization, Analysis And Progress Monitoring, Megan Wiser, Liam A'Hearn, Christopher Tamayo, Liam Kelly

Computer Science and Engineering Senior Theses

This paper presents an innovative approach to enhance at-home physical therapy exercises through the development of a wearable motion tracking system. The proposed system utilizes motion tracking bands worn by patients during exercises, specifically focusing on a squat jump for the initial phase of the project. The bands, placed around the ankle and knee, monitor the alignment of the user's motion and transmit data via Bluetooth Low Energy (BLE) to a dedicated webpage. This webpage integrates real-time data analysis, offering immediate feedback to users, enabling them to monitor their form and track progress over time. The collected data is stored …

Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo

Theses and Dissertations

New technologies are being introduced at a rate faster than ever before and smaller in size. Due to the size of these devices, security is often difficult to implement. The existing solution is a firewall-segmented “IoT Network” that only limits the effect of these infected devices on other parts of the network. We propose a lightweight unsupervised hybrid-cloud ensemble anomaly detection system for malware detection. We perform transfer learning using a generalized model trained on multiple IoT device sources to learn network traffic on new devices with minimal computational resources. We further extend our proposed system to utilize federated learning …

How Ai And Digitalization Can Help To Improve Managing Projects In Lean Manufacturing & Supply Chain Environment, Abhijeet Hange

Harrisburg University Dissertations and Theses

This research examines how digitalization and machine learning (ML) revolutionize project management in supply chain and lean manufacturing settings. To improve the efficiency and caliber of their operational procedures, Acuity Brands, Amazon, Walmart, Apple, and other medium-sized businesses—which are at the center of today's competitive markets—are resorting to automation and digitalization. With an emphasis on the fusion of digital and artificial intelligence (AI), the paper explores how successful transformation initiatives are essential to the automation and digitization of manufacturing and distribution hubs. The study uses quantitative methods approach to evaluate the benefits and drawbacks of these technology integrations by integrating …

Assessment For Sustainable Transportation Systems In Egypt, Hagar Mohamed Fares

Theses and Dissertations

The assessment of a sustainable transportation system poses a significant challenge for local governments, as it dramatically influences the promotion of environmentally friendly modes of transportation with the objective of safeguarding the public's welfare and health in their daily activities. The integration of such assessment should be implemented from the initial stages of system planning to its operational and maintenance phases, particularly in light of the increasing number of urban development activities nationwide. In order to facilitate decision-making processes, it is imperative to establish an efficient rating system that incorporates cultural and social constraints and comprehensively considers the three dimensions …

Pixel-Mps: Stochastic Embedding And Density-Based Clustering Of Image Patterns For Pixel-Based Multiple-Point Geostatistical Simulation, Adel Asadi, Snehamoy Chatterjee

Michigan Tech Publications

Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and …

Physical Effects On The Worst-Case Delay Analysis And Signal Integrity Of Buses And Spirals, Mahmoud Mahany

Theses and Dissertations

Physical effects have a significant impact on the IC design which will be investigated in this thesis. Moving toward advanced technology nodes, magnetic effects become more dominant than capacitive effects. As the dimensions of the devices go down and the interconnect manipulates the circuit behavior more and more. Cross talking and voltage drops are affecting the design heavily, however - going to the full electromagnetic point of view - current return path (CRP) adds significant parasitics to the performance of the chip. Neglecting the CRP gives wrong intuition and simulation of the designs, especially that the environment and surroundings can …

A Comprehensive Study On The Impact Of Human Hair Fiber And Millet Husk Ash On Concrete Properties: Response Surface Modeling And Optimization, Naraindas Bheel, Muhammad Alamgeer Shams, Samiullah Sohu, Abdul Salam Buller, Taoufik Najeh, Fouad Ismail Ismail, Omrane Benjeddou

Department of Civil and Environmental Engineering: Faculty Publications

Revolutionizing construction, the concrete blend seamlessly integrates human hair (HH) fibers and millet husk ash (MHA) as a sustainable alternative. By repurposing human hair for enhanced tensile strength and utilizing millet husk ash to replace sand, these materials not only reduce waste but also create a durable, eco-friendly solution. This groundbreaking methodology not only adheres to established structural criteria but also advances the concepts of the circular economy, representing a significant advancement towards environmentally sustainable and resilient building practices. The main purpose of the research is to investigate the fresh and mechanical characteristics of concrete blended with 10–40% MHA as …

Chemical Herding As A Multiplicative Factor For Top-Down Manipulation Of Colloids, Mark N. Mcdonald, Douglas R. Tree, Cameron K. Peterson

Faculty Publications

Colloidal particles can create reconfigurable nanomaterials, with applications such as color-changing, self-repairing, and self-regulating materials and reconfigurable drug delivery systems. However, top-down methods for manipulating colloids are limited in the scale they can control. We consider here a new method for using chemical reactions to multiply the effects of existing top-down colloidal manipulation methods to arrange large numbers of colloids with single-particle precision, which we refer to as chemical herding. Using simulation-based methods, we show that if a set of chemically active colloids (herders) can be steered using external forces (i.e., electrophoretic, dielectrophoretic, magnetic, or optical forces), then a larger …

Emergent Magnetism And Hyperthermia In Phase- And Size-Tunable Iron Oxide Nanostructures, K Mudiyanselage Tharindu Supun Bandara Attanayake

USF Tampa Graduate Theses and Dissertations

Iron oxide nanoparticles (IONPs) hold immense potential across diverse fields, from spintronics, magnetic hyperthermia to drug delivery, biodetection, and magnetic resonance imaging. Unlocking these applications hinges on our ability to tailor the magnetic properties of IONPs, and this dissertation presents novel and powerful approaches for enhancing magnetic and hyperthermic responses of multiphase iron oxide nanostructures. This is achieved by manipulating their phase volume fraction, size, and shape with a focus on the magnetic tunability of nanostructures via strategic control over structural and environmental parameters. The aforementioned has been achieved by the following three key approaches: (i) phase-tunability of iron oxide …

Electrochemical Identification Of Metal Chlorides In Eutectic Licl-Kcl Without Prior Knowledge Of Analyte Identities, Tyler Williams, Jason Torrie, Mark Schvaneveldt, Ranon Fuller, Greg Chipman, Devin Rappleye

Faculty Publications

The identities of unknown analytes within four eutectic LiCl-KCl melts were determined using electrochemical methods, simulating the uncertainty of electrochemically probing an electrorefiner salt bath or molten salt nuclear reactor. With a variety of electrochemical methods (e.g. cyclic voltammetry, chronopotentiometry, and square-wave voltammetry), and electroanalytical techniques (e.g. semi-differentiation), every analyte was positively identified, although one false positive occurred because of an unexpected chemical interaction. This study highlights some remaining challenges for the use of electrochemical sensors in nuclear material control and accountability in molten salts: (1) quantification of analytes without the use of calibration curves (e.g. error in property values, …

A Machine Learning Framework For Predicting Fabrication Hours For Industrial Steel Structure Projects, Dalia Ibrahim

Theses and Dissertations

Construction projects are considered high risk projects especially due to their required large capital making them require extreme attention in estimation as overestimating a project will lead to losing bids and underestimating them will lead to incurring more costs than budgeted resulting in losses. However, estimators are often faced with very tight timelines to finish their estimates leading them to primarily rely on their experience disregarding some crucial factors resulting in inaccurate estimates. In the steel structures industry, the steel fabrication phase accounts for 30 to 40% of the overall project cost; in addition, the steel industry is labor driven; …

An Efficient Method For Recognition Of Human-In-Motion Action Based On Foot-Lift Features, Khin Cho Tuna, Hla Myo Tunb

ASEAN Journal on Science and Technology for Development

In the age of Industry 4.0, recognition of human moving actions becomes an essential element in smart surveillance systems at public places. The requirement of a large number of frames, view dependency and the requirement of big database are still challenging issues for human action recognition in real-time applications. This study proposes an efficient method for basic moving actions, “Walking” and “Running”, based on foot-lift features aiming at the recognition of ongoing moving action by reducing the number of frames required. The plane of moving path and foot-lift are estimated during the action by means of BLOB analysis. The performance …

Hiv3: An Efficient Beehive Monitoring System, Jack Ursillo, Aneal Kuverji, Connor Merhab, Anshuman Sahu

Computer Science and Engineering Senior Theses

Beehive monitoring plays a major role in ensuring the health of beehives by checking for overpopulation or underpopulation within a hive. Beehive monitoring provides beekeepers with the opportunity to take action and save the hive before the problem becomes irreversible. Most solutions are too expensive for everyday beekeepers and lack elements of sustainability, making it impractical for small scale beekeepers. In this thesis, we propose a solution to this problem, demonstrating its sustainability and user-friendliness, which enables us to effectively reach a larger consumer market. We support these claims through the use of sustainable systems such as using a solar …