Digital Commons Network^™

Smart Irrigation System Using Iot And Lstm For Optimal Water Management, Farley Y. Ruiz

Electrical Engineering Theses - Archive

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web …

Power Quality Event Diagnosis Using Multi-Rate Neural Networks, Jordan D. Lloyd

Electrical Engineering Theses - Archive

Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) …

Immersive Executive Functions Assessment System (Iexec): Integrating Embodied Cognition And Virtual Reality, Hamza Reza Pavel

Computer Science and Engineering Dissertations - Archive

Executive functions (EFs) are higher-order cognitive processes that include working memory, inhibitory control, and cognitive flexibility. These higher-order processes facilitate the achievement of goal-directed behavior and enable both adaptive decision-making and emotional regulation. Traditional EF assessment tools depend on static pen-and-paper tasks or basic computer-based tasks, which fail to capture real-world cognitive complexity and dynamics. Some of these assessment tools are specifically geared towards children or older adults, while others are more generic and designed to be used for people of all ages. This dissertation addresses these limitations by introducing iExec: The Immersive Executive Functions Assessment System, which functions as …

Enabling Energy And Water Sustainability Through Out-Of-Band Emi Sensing And Infrastructure Modeling, Pranjol Sen Gupta

Computer Science and Engineering Dissertations - Archive

As demand for Internet and cloud services surges, data centers have emerged as critical infrastructure—but they are also among theworld’s most energy- andwater-intensive facilities. Effective power management, particularly at the server level, is essential for improving efficiency, reliability, and sustainability. However, server-level power monitoring remains uncommon due to the high cost of hardware instrumentation and the intrusiveness of software-based solutions, especially in shared colocation environments. My research introduces a novel, low-cost, and non-intrusive method for server-level power monitoring using conducted electromagnetic interference (EMI). By analyzing EMI signals captured from higher levels in the power distribution network, this approach estimates individual …

Bibliometric And Visualisation Analysis Of Biomass Burning Research In Thailand (1997-2024), Weerachon Sawangproh, Arika Bridhikitti

Applied Environmental Research

Biomass burning is a major driver of air pollution and climate-related impacts in Thailand, but a national-level synthesis of research efforts is lacking. This study conducts a comprehensive bibliometric and qualitative review of 256 peer-reviewed publications from 1997–2024, using Scopus-indexeddata to explore research trends, thematic patterns, and existing gaps. The analysis reveals a strong emphasis on atmospheric chemistry, air quality modeling, and remote sensing, which collectively account for more than 60% of the research landscape.Studies frequently focus on pollutants such as PM2.5,PAHs, carbon monoxide, and heavy metals, with biomass burning identified as a dominant source, especiallyduring the dry season in …

Public Attention Index Of Air Pollution Exposure From Pm2.5 In Thailand, Pailin Suntigul, Sittidaj Pongkijvorasin

Applied Environmental Research

Thailand faces a persistent PM2.5 challenge, with pollution levels consistently exceeding the World Health Organization’s guidelines. In response, the Thai government has made air pollution a national priority, implementing various policies and city-level plans to reduce exposure. These policies are often temporary and do not fully address root causes, whereas the limited monitoring coverage across Thailand restricts a comprehensive assessment of PM2.5 exposure severity. This study addresses the data gap by constructing a public attention index for PM2.5, using internet search data from Google Trends, to quantify public awareness and concern regarding PM2.5 exposure. This index aims to improve the …

Evaluation Of The Maneuverability Of An 85m Fishing Trawler (Focusing On Dynamic Stability), Chun-Ki Lee, Su-Hyung Kim

Journal of Marine Science and Technology–Taiwan

Most fishing vessels are less than 100 meters in length, so IMO maneuverability standards do not apply. As a result, studies on the maneuverability of fishing vessels are considerably fewer compared to those on merchant ships. However, with technological advancements, the size of fishing vessels continues to grow, and as more fishermen are on board, accidents could lead to fatal casualties. In response, the authors determined that research on the maneuverability of fishing vessels is urgently needed. Thus, the authors conducted a maneuverability evaluation on a fisheries training vessel with the hull shape of a fishing trawler, 85 meters in …

Toward Strategy Identification And Subtask Decomposition In Task Exploration, Tom Odem

Master's Projects

This research builds on work in anticipatory human-machine interaction, a subfield of human-machine interaction where machines can facilitate advantageous interactions by anticipating a user’s future state. The aim of this research is to further a machine’s understanding of user knowledge, skill, and behavior in pursuit of implicit coordination. A task explorer pipeline was developed that uses clustering techniques, paired with factor analysis and string edit distance, to automatically identify key global and local strategies that are used to complete tasks. Global strategies identify generalized sets of actions used to complete tasks, while local strategies identify sequences that used those sets …

Plasmonic Nanopore Sensing For The Characterization Of Viruses And Biomolecules, Scott Renkes

Bioengineering Dissertations - Archive

The rapid advancement of nanomedicine has enabled increasingly precise nanotherapeutic platforms, including targeted nanoparticles, monoclonal antibodies, and viral gene delivery vectors. As these technologies mature, quality control and analytical characterization have emerged as critical bottlenecks, particularly in systems where functional outcomes depend on one-to-one molecular or particle–biological interactions. Conventional ensemble-averaged techniques obscure heterogeneity that is often central to therapeutic efficacy, safety, and manufacturability. A prominent example is adeno-associated virus (AAV)–based gene therapy, where production yields heterogeneous populations of empty, partially loaded, and fully loaded capsids. Genome fill percentage and integrity directly influence therapeutic efficacy and dosing accuracy, while limited understanding …

Life Cycle Assessment Approach For Trenchless Cured-In-Place Pipe Sewer Renewal Method, Shiva Mandari

Civil Engineering Theses - Archive

The advancement of underground infrastructure is vital to the development of modern society and continues to inspire technological progress. As environmental concerns grow, innovative approaches to pipeline rehabilitation are increasingly emphasizing sustainability. Assessing and contrasting the environmental effects of renewing existing pipelines versus complete replacement is key to fostering eco-conscious improvements in subsurface utility systems. Cured-in-Place Pipe (CIPP) is a commonly used trenchless method for rehabilitating pipelines. It involves inserting a flexible liner, saturated with resin, into the existing pipe. This insertion can be achieved through inversion using air or water pressure, or by mechanically pulling and inflating the liner. …

Characterization Of Dc Arc Flash Events Generated By Electrochemical Energy Storage, Nicolaus E. Jennings

Electrical Engineering Dissertations - Archive

The increasing rise in the use of electrochemical energy storage (ECES) like in the form of valve regulated lead acid (VRLA) batteries, lithium-ion (Li-ion) batteries, electric double layer capacitors (EDLC), and metalized film, oil filled capacitors prompt new challenges concerning electric worker safety. The primary safety hazards associated with ECES are electric shock and arc flash. The electric shock hazard is well understood to the extent where it is known what potential and exposure duration will cause levels of pain and ultimately fatality. Various personal protective equipment (PPE) like insulating gloves allow electric workers to perform maintenance with sufficient protection …

Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring, Marina Materikina

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Despite the numerous research studies and interest in the non-intrusive load monitoring (NILM) area to improve energy efficiency, the problem of accurate and precise disaggregation of electrical devices has not been solved yet. The goal of our research is to build a method with a focus on higher accuracy on complex state-based appliances, which most approaches struggle to detect due to their power signal complexity and low consumption. Our approach is NILM with data-driven signatures (DS), with the ability to potentially predict power usage over time that would work great for suitable applications such as demand response, anomaly detection, and …

A Multi-Method Geophysical Approach For Complex Shallow Landslide Characterization, Mohammadyar Rahimi, Clinton Wood, Meersad Fathizadeh, Salman Rahimi

Civil Engineering Faculty Publications and Presentations

This case study demonstrates the value of combining multiple non-invasive geophysical methods to characterize a landslide along Highway 7 near Jasper, Arkansas, USA. Geophysical testing was conducted using Multichannel Analysis of Surface Waves (MASW), Horizontal to Vertical Spectral Ratio (HVSR), and Electrical Resistivity Tomography (ERT), supplemented by select soil borings. The geophysical investigation aimed to provide a high-resolution, near-continuous view of subsurface conditions, including bedrock depth and the location of the groundwater table or highly saturated zones within the slide area. These factors are important contributors to slope instability. The MASW results revealed a highly variable depth to weathered bedrock …

A New Deep Decoder Type Cascaded Feedforward Neural Network For Critical Heat Flux Prediction In Power Reactors, Rehan Zubair Khalid, Atta Ullah, Asifullah Khan, Mansoor Hameed Inayat, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Critical Heat Flux (CHF) is a fundamental parameter for ensuring the performance, reliability, safety, and economic viability of water-cooled nuclear reactors. Despite its significance, the absence of a deterministic theory for CHF prediction poses a substantial challenge in thermal engineering. Consequently, various experimental and numerical models have been developed, yet no universally accepted model comprehensively addresses the wide range of flow conditions encountered in practical applications. The accurate prediction of CHF remains a complex and critical task. This work introduces a novel Deep Decoder Type Cascaded feedforward Neural Network (DD-CFNN) to predict CHF across a broad spectrum of operating conditions. …

Development Of Self-Healing Polymer From Bio-Based Benzoxazine Blend With Poly(Ethylene Glycol) For Anti-Biofouling Applications, Pharaporn Yodkum

Chulalongkorn University Theses and Dissertations (Chula ETD)

Biofouling remains a significant challenge in marine industries, as conventional coatings often lose their antifouling efficacy once they become damaged. Incorporating self-healing capabilities into antifouling coatings has therefore emerged as a promising strategy to extend service life and maintain long-term performance. In this study, self-healing polymers were developed based on a bio-derived benzoxazine resin (E-fa), synthesized from renewable precursors including eugenol (a phenol source), furfurylamine (an amine source), and paraformaldehyde, and subsequently blended with poly(ethylene glycol) (PEG). The blends were formulated with varying weight ratios (95/5, 90/10, 85/15, and 80/20 wt/wt%) and PEG molecular weights (PEG-600, PEG-1500, and PEG-4000). This …

Leveraging Deep Learning For The Automated Detection Of Hemophilic Arthropathy In Knee Radiographs, En-Wei Lin, Edzer Wu, Ming-Ching Shen, Shao-Li Han

Rehabilitation Practice and Science

Background/Purpose:

Hemophilic arthropathy (HA) is a progressive joint disease caused by recurrent hemarthroses in patients with hemophilia. Early radiographic identification of HA is critical for timely intervention and long-term joint preservation. This study aimed to develop and evaluate deep learning models for automated detection of HA in knee radiographs.

Methods:

A retrospective dataset of 849 knee radiographs (AP and lateral views) was used to train three convolutional neural networks: ResNet-34, DenseNet-121, and EfficientNet-B0. Model performance was assessed using standard classification metrics, including accuracy, precision, recall, F1-score, and AUC. Interpretability was evaluated using Grad-CAM heatmaps.

Results:

DenseNet-121 achieved balanced performance on …

New Insights Into Fertilisation With Animal Manure For Annual Double-Cropping Systems In Nitrate-Vulnerable Zones Of Northeastern Spain, Dolores Quilez, Monica Guillen, Marta Valles, Arturo Dauden, Beatriz Moreno-Garcia

Biological and Agricultural Engineering Faculty Publications and Presentations

Maize double-cropping production systems in Mediterranean areas have a great nitrogen extraction capacity and high nitrogen (N) requirements. This study aims to assess whether in these farming systems, animal manure can be applied, using adequate management practices, at levels exceeding the maximum annual amount of livestock manure established in the European Nitrate Directive for vulnerable zones (170 kg N ha-1) without increasing the risk of water nitrate contamination. We compare the risk of nitrate leaching under two fertilisation strategies, one with synthetic fertilisers and the second with a maximised application of pig slurry, exceeding the limits of the EU Nitrate …

Adaptation For Whom? Understanding The Impacts Of Dyke Policies On Small-Scale Farmers In Ca Mau Province Of Vietnam, Nguyen Minh Quang, Nguyen Van Minh, Nguyen Vo Chau Ngan, Martha B. Lerski

Publications and Research

Low-lying deltas worldwide are extremely vulnerable to sea-level rise. As the risks of climate-induced flooding become increasingly clear, the thirst for infrastructure-driven adaptation increases. This highlights a patent need to understand the impacts of engineering adaptation solutions, especially those targeting built structures such as dykes and sluices, on farmers from a bottom-up, farmer-centred perspective. The study addresses this call by examining cases where dyke policies, while successful in some dimensions, have undermined small-scale farmer livelihoods in Ca Mau ― Vietnam’s southernmost rural province. It identifies areas in need of attention and adds empirical data related to this group of farmers. …

Powersynth 2: Automated Power Electronics Physical Design Synthesis With Custom And Heterogeneous Components, Mehran Sanjabiasasi, Alan H. Mantooth, Yarui Peng

Electrical Engineering and Computer Science Faculty Publications and Presentations

Electronics Design Automation (EDA) has shown significant importance in the power electronics industry. As power electronic circuits become more complex, the traditional trial-and-error approach in physical design becomes less effective and time-consuming. Novel packaging technologies and intelligent physical design automation solutions are crucial to overcome these challenges and produce reliable solutions. PowerSynth 2 is an EDA tool for generating and optimizing power module layouts. To extend the layout synthesis capability beyond power modules, the layout engine needs to consider various custom components such as capacitors, inductors, and gate drivers. This research presents a novel framework for the layout synthesis process …

Performance Evaluation Of Managed Switch Configurations For Secure And Efficient Plc-Based Industrial Automation Networks, Ahmed Salama

All Graduate Theses, Dissertations, and Other Capstone Projects

This thesis explores how managed switches can improve network performance and security in PLC-based industrial systems. Using simulations in GNS3 and Cisco Packet Tracer, and packet analysis via Wireshark, the study compares unmanaged and managed switch configurations. Redundancy protocols, particularly STP and RSTP, are evaluated under failure scenarios. Results show that RSTP offers faster recovery times, making it more suitable for time-sensitive environments. Additionally, managed switch features like VLANs, port security, and MAC filtering significantly reduce vulnerabilities and improve network segmentation. The findings highlight the importance of incorporating both cybersecurity and redundancy in industrial network design as systems move toward …

‘Waves Of Imagination’ Unconditional Spectogram Diffusion Using Diffusion Architecture., Rahul Vanukuri

Computer Science and Engineering Theses - Archive

The swift evolution of wireless communication technologies,particularly in the field of rf signals or in CBRS bands,demands increasingly sophisticated signal processing techniques to ensure efficient transmission, reception, and spectrum management.Traditional approaches to signal generation and reconstruction, although effective in controlled environments, often struggle to cope with the challenges presented by real-world noisy conditions, hardware constraints, and limited access to large-scale datasets. In response to these limitations, this thesis explores the application of diffusion models—a class of generative models known for their ability to produce high-fidelity samples—to the domain of spectrogram generation for communication signals.

Different from conventional strategies to simulate …

Csc 22100 Software Development Laboratory Homework Assignment #1, Mitch Gershonowitz

Open Educational Resources

This is the first homework assignment for an Introduction to Java Programming course, which requires the students to write Java code using industry-accepted conventions. The assignment requires the student to write a Java method that accepts a date in an internationally-recognized format and returns a String object in Julian Date Format (JDF) with format YYDDD.

Csc 22100 Software Development Laboratory Homework Assignment #3, Mitch Gershonowitz

Open Educational Resources

This is the third homework assignment for an Introduction to Java Programming course, which requires the students to write Java code using industry-accepted conventions. The assignment requires the student to write a Java classes and interfaces that demonstrate usage of passing lambda expressions to methods, as well as demonstrating polymorphism and inheritance.

Assessing Deep Learning Models And Hyperparameter Optimization For Stable Time-Series Electricity Load Forecasting, Sukma Patrya, Aji Prasetya Wibawa, Aripriharta Aripriharta

Knowledge Engineering and Data Science

Long-term electricity load forecasting plays an important role in ensuring system reliability, optimizing energy management, and making operational plans in face of continuously rising electricity demands. This study suggests a complete deep learning method for univariate forecasting of future electricity loads based on climatology and electricity consumption data for the period between 2019 and 2023. The initial dataset was cleaned, normalized, and partitioned chronologically into train/test datasets. Four train/test split cases (20/80, 40/60, 60/40, 80/20) were considered to explore the impact of different levels of historical data availability on the performance of the suggested framework from data-poor to data-rich situations. …

Digital Twins For Flexible Manufacturing, Drew Brandon Sander

Theses and Dissertations

With the establishment of Digital Twins as an enabling technology for Industry 4.0, it is time to transition from the previous foundations to works that implement and highlight the capabilities Digital Twins present. The Industry 4.0 paradigm has recentered the focus on customizability, leading to reemerging goals of flexibility within manufacturing. Empowered through new cyber-physical systems, new enabling technologies are driving the revolution. To identify how Digital Twins as an enabling technology contribute to flexibility within manufacturing, a comprehensive investigation is conducted along with a detailed case study. A systematic literature review is conducted to analyze quality Digital Twin implementations …

Ai Powered Legal Decision Support System, Alisha Rath

Master's Projects

The large volume of legal cases presented by judicial professionals has made it
challenging to study and predict results. With advances in research methods and
technology, predicting law cases in a more accurate manner has become an important
trend. Prediction tools based on AI may help manage a large number of legislative
texts and documents that cannot possibly be fully read, reduce the number of cases
to be seen, and give accurate outcomes of how cases may turn out. Now, when
we look into the current AI legal prediction tools in this domain, they mostly lack
efficiency and interpretability, the …

Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb

Master's Projects

Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …

Visionmate: Ai-Powered Image Captioning Web Application, Sai Anoushka Kokku

Master's Projects

VisionMate is a web application that generates captions for camera-captured images. It is designed
to assist users with visual impairments by converting visual input into spoken and written text. The
application uses the GIT-base model from Hugging Face, which processes the image and returns a
descriptive caption. Users can take a picture using the device camera—either via webcam on
desktop or the native camera interface on mobile. The app provides audio output using the
SpeechSynthesis API and uses full-screen tap interaction to simplify accessibility.

The frontend is implemented in React.js, and the backend is built with FastAPI. The backend calls …

Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula

Master's Projects

Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior …

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …