Engineering Commons^™

Automated Concrete Bridge Deck Inspection Using Unmanned Aerial Systems Collected Data: A Machine Learning Approach, Rojal Pokhrel

Master's Theses

Many national infrastructures, particularly bridges, are becoming older and older day by day. Their safe operation and maintenance require routine monitoring and inspection. There are lots of bridge inspection specifications and manuals to guide inspectors to give the proper recommendations about the bridge structure and safety. These manuals and specifications require bridge inspectors to manually inspect the bridge and recommend maintenance and repair tasks referring to the specific criteria available in the manuals. In time, the application of technology makes many Unmanned Aerial Systems (UAS) advancements during the phase of data collection. For the image processing and analysis of collected …

Solar Car Radio Control Car Curriculum (9-10 Grade), Divya Baranwal, Ashish Gandhi

Multidisciplinary Studies Research

In this unit, high school students will comprehend the concepts of solar energy and conversion of sunlight into electricity; and basic electronics, circuitry principles, and electric components like microcontrollers, resistors, sensors, and LEDs; basic programming hands-on experience to write code for microcontrollers and programming functionalities like controlling the car’s movement, and the conversion of potential energy to kinetic energy on various inclined ramps, delving into scientific principles. They will understand the basics of machine learning, robotics, soldering, engage with hands-on experiences in engineering design principles, hands-on experiences starting from building car evolution models throughout a week. This unit focus to …

Studying The Performance Of Object Recognition With Fusion Of Visible Light And Infrared Images With Neural Networks, Plamen Petkov

Doctoral Dissertations and Master's Theses

Neural networks have been used for object detection and recognition in both color and intensity camera images. As the use of infrared cameras, colloquially termed thermal cameras, has increased and costs have decreased, object detection and recognition in infrared camera images have been increasingly studied. An infrared image is treated as an intensity image, just like a grayscale camera image, except the intensity corresponds to infrared radiation instead of visible light. The information provided by these two types of images are different, especially in different lighting and environmental situations, and some types of objects are more easily recognized in visible …

Machine Learning Approaches To The Exploration Of Ammonia Synthesis Catalysts Utilizing Experimental Data, Rasika Jayarathna

Theses and Dissertations

Traditional heterogeneous catalyst discovery process relies upon human expert-guided experiments based on domain knowledge and human intuition supported by catalyst characterization. However, this process is time and resource-consuming and suffers from human bias. Machine learning (ML) models trained on experimental data have the potential to significantly accelerate this discovery process by guiding experiments to optimize the catalyst performance and increase the understanding of the catalysts. Ammonia synthesis catalysts are a class of catalysts widely investigated in the literature, which has generated decades of experimental data. Moreover, ammonia has been gaining attention since it can be used as a carbon-free next-generation …

Advancing Adversarial Audio: Human-In-The-Loop Black-Box Attacks, Rui Duan

USF Tampa Graduate Theses and Dissertations

Adversarial audio attacks pose significant security challenges to real-world audio applications. Attackers may manipulate speech to impersonate a speaker, gaining access to smart devices like Amazon Echo. In audio applications, there are two key areas: music and speech. In music, most attackers create a small noise-like perturbation on the original signal to evade copyright detection. However, this method degrades music's perceived quality for human listeners. In the speech, creating an adversarial example often requires many queries to the target model, a process too cumbersome for practical use in real-world scenarios, like interacting with smart devices numerous times.

In this dissertation, …

Autonomous Microgrid System, Xavier Kuehn, Brian Xiong

Computer Science and Engineering Senior Theses

Microgrids have made a revolutionary change in the realm of energy distribution due to the features that they offer, including localized, resilient, and sustainable energy solutions. Operating renewable resources in a microgrid while maintaining generation-load balance and acceptable voltage-frequency limits has been an open research problem. This thesis presents smart python agents for microgrid systems to automate the operations and control of microgrid renewable resources in an effort to provide resilient solutions to the intermittence issues that could potentially arise within the microgrid energy system. The smart agents operate the microgrids by not only integrating the use of renewable energy …

Exploring The Role Of Construction Technology Copilots, James S. Thielmann Jr.

Construction Management

In the rapidly evolving construction industry, the adoption of software solutions is essential for enhancing efficiency, reducing costs, and ensuring timely project delivery. This paper assesses the effectiveness and usefulness of existing construction software solutions, focusing on their functionalities, strengths, and limitations. Among the prominent solutions gaining traction are Trunk.Tools, Constructable.ai, and ProjectEngineer.ai. The construction industry, although historically slower to adopt new technologies, stands to gain a great deal from these tools in terms of project management and performance. This research includes a thorough examination of ProjectEngineer.ai's reach in the market, feature development, and growing uptake among experts in the …

Generative Data Augmentation: Using Dcgan To Expand Training Datasets For Chest X-Ray Pneumonia Detection, Ryan D. Maier

Master's Theses

Recent advancements in computer vision have demonstrated remarkable success in image classification tasks, particularly when provided with an ample supply of accurately labeled images for training. These techniques have also exhibited significant potential in revolutionizing computer-aided medical diagnosis by enabling the segmentation and classification of medical images, leveraging Convolutional Neural Networks (CNNs) and similar models. However, the integration of such technologies into clinical practice faces notable challenges. Chief among these is the obstacle of acquiring high-quality medical imaging data for training purposes. Patient privacy concerns often hinder researchers from accessing large datasets, while less common medical conditions pose additional hurdles …

Anomaly Detection In Heterogeneous Iot Systems: Leveraging Symbolic Encoding Of Performance Metrics For Anomaly Classification, Maanav Patel

Master's Theses

Anomaly detection in Internet of Things (IoT) systems has become an increasingly popular field of research as the number of IoT devices proliferate year over year. Recent research often relies on machine learning algorithms to classify sensor readings directly. However, this approach leads to solutions being non-portable and unable to be applied to varying IoT platform infrastructure, as they are trained with sensor data specific to one configuration. Moreover, sensors generate varying amounts of non-standard data which complicates model training and limits generalization. This research focuses on addressing these problems in three ways a) the creation of an IoT Testbed …

A Study On Privacy Over Security And Privacy Enhancing Networks, Everett Lee Conway

Master's Theses

With rapid developments in communication technologies and awareness of security and privacy risks online, Security and Privacy Enhancing Networks (SPENs) have become increasingly popular. Especially during the COVID-19 pandemic, workplaces encouraged employees to take additional security measures, such as VPNs. In this work, we conduct a comprehensive study on website fingerprinting attacks. A comprehensive system model and threat model based on two types of SPENs (Virtual Private Networks and Tor Networks) are presented. Moreover, we demonstrate a website fingerprinting attack by ethically collecting website fetch data and analyzing the collected data using five different machine learning classification models including k …

Machine Learning For Graph Algorithms And Representations, Allison Gunby-Mann

Dartmouth College Ph.D Dissertations

This thesis explores a variety of common graph theoretic problems from a machine learning perspective. The topics covered include fundamental network problems such as distance approximation, distance sensitivity, community detection, cross-network alignment, and graph embedding dimension reduction. These projects are unified by the theme of machine learning on graphs, graph embeddings, and representations of graphs.

Data-Driven Insights Into Spatial Patterns And Disease Etiologies Of White Matter Hyperintensities, Sugandha Roy

McKelvey School of Engineering Graduate Student Theses & Dissertations

In this thesis, we have applied Orthogonal Projective Non-Negative Matrix Factorization (opNMF) to identify spatial patterns of white matter hyperintensities (WMH) within UK Biobank's imaging data. Our selection criteria excluded subjects with a history of neurological, mental, and specific cerebrovascular conditions, allowing us to focus on WMH patterns in a healthy aging population. We have interrogated the association of location-specific WMHs with a variety of demographic, clinical, and genetic factors. Our multivariable regression analysis evaluates the strength and nature of the associations between these factors and WMH distribution. The analysis integrates variables such as age, sex, smoking habits, medication usage …

Development Of A Rapid Bacterial Quantification Method Based On Droplet Microfluidics, Shawn Bliss

Electronic Theses and Dissertations

The capacity to identify bacteria quickly and accurately is critical for applications such as medical diagnosis, environmental monitoring, and food safety. For instance, sepsis diagnosis requires blood cultures that take as long as 1-5 days, preventing timely intervention and increasing mortality rate. Here, we propose the joint use of microfluidics and a machine learning algorithm for rapid bacterial cell capture and quantification. The combination of high-throughput droplet microfluidics and a support vector machine (SVM) enables analysis and quantification of bacterial samples within as short as 4 hours. We have performed successful encapsulation of pathogenic bacteria such as E. coli, …

Evaluating Neuroimaging Modalities In The A/T/N Framework: Single And Combined Fdg-Pet And T1-Weighted Mri For Alzheimer’S Diagnosis, Peiwang Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

With the escalating prevalence of dementia, particularly Alzheimer's Disease (AD), the need for early and precise diagnostic techniques is rising. This study delves into the comparative efficacy of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and T1-weighted Magnetic Resonance Imaging (MRI) in diagnosing AD, where the integration of multimodal models is becoming a trend. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in combination, within the AD continuum. Our analysis, under the A/T/N framework's 'N' category, reveals that FDG-PET consistently outperforms T1w-MRI across …

Machine Learning For Intrusion Detection Into Unmanned Aerial System 6g Networks, Faisal Alrefaei

Doctoral Dissertations and Master's Theses

Progress in the development of wireless network technology has played a crucial role in the evolution of societies and provided remarkable services over the past decades. It remotely offers the ability to execute critical missions and effective services that meet the user's needs. This advanced technology integrates cyber and physical layers to form cyber-physical systems (CPS), such as the Unmanned Aerial System (UAS), which consists of an Unmanned Aerial Vehicle (UAV), ground network infrastructure, communication link, etc. Furthermore, it plays a crucial role in connecting objects to create and develop the Internet of Things (IoT) technology. Therefore, the emergence of …

Analyzing Information Cascades Through Machine Learning And Data Analytics, Betul Agirman

Honors Scholar Theses

In today's digital age, social media platforms have become pivotal in influencing public opinion and behavior, with information spreading being both beneficial and detrimental. This rapid spread is typically called an information cascade, and they are important in further understanding social influence, managing misinformation, and even predicting potential trends of public responses. With social media, people are connected so easily to one another like a network, wherein it becomes possible for them to influence each other’s behavior and decisions. Utilizing a dataset from Weibo that spans critical periods of the COVID-19 outbreak, this study integrates machine learning and data analytics …

Addressing Data Limitations Of Commercial Waterways Via Machine Learning And Stochastic Optimization, Sanjeev Bhurtyal

Graduate Theses and Dissertations

Freight transportation is a crucial component of the US economy, with truck, rail, and water contributing significantly to the Gross Domestic Product (GDP). However, challenges arise in maintaining infrastructure capacity to accommodate the growing tonnage and value of freight transportation. Waterways offer high efficiency and environmental friendliness, with the capacity to transport substantial cargo volumes. Utilizing waterways can alleviate bottlenecks in landside transportation, accommodating the rising tonnage of commodity flow in the US. Despite the advantages of waterway transportation, the data limitations hinder informed decision-making regarding port operation and infrastructure investment. The lack of granularity and timeliness in available data …

Using Convolutional Neural Networks For Autonomous Drone Navigation, Joshua Jowers

Industrial Engineering Undergraduate Honors Theses

Unmanned Aerial Vehicles (UAVs), more commonly known as drones, serve various purposes, notably in military applications. Consequently, there arises a need for navigation methods impervious to intercepted signals [1]. Previous research has explored numerous solutions, including machine learning. This paper delves into a specific machine learning approach employing a Convolutional Neural Network (CNN) to discern image locations [2]. It elucidates the conversion of a CNN model between two machine learning libraries and presents results from multiple experiments examining parameters and factors influencing the approach's efficacy. These experiments encompass testing different data sources, image quantities, and processing pipelines to gauge their …

Head Impact Measurement Using Piezoelectric Sensors, Huda Abdulla Alnuaimi

Theses

The importance of safety measures cannot be overstated, especially when it comes to protecting the human head. Head injuries can have severe, life-altering consequences, as the head is crucial for controlling the entire body. Unlike machines that store data, the human brain's capacity to retain thoughts and memories can be significantly affected by even a single injury. This thesis introduces a method for predicting the specific area of the head that might be injured during an impact. The prediction is based on the intensity and duration of the impact. The innovation of this thesis lies in the use of piezoelectric …

Implementation Of Explainable Ai For Bearing Fault Classification, Mohammad Mundiwala

Honors Scholar Theses

It is difficult to overstate the impact of artificial intelligence (AI) over the past decade. The rapid expansion of machine learning has stimulated a race to deploy AI in all facets of life, one such domain being machine health monitoring. There is no doubt that machine learning excels in prediction accuracy, but oftentimes, these models are cryptic and fail to provide valuable insight into their decisions. This paper presents an overview of a neural network and what it means to learn. Next, two distinct Explainable AI (XAI) techniques will be presented: Gradient Class Activation Mapping and SimplEx . Finally, these …

Multi-Task Learning For Hybrid Communication Waveforms: Exploring Model Enhancement Techniques And Establishing Task Relationships, Saksham Dewan

Legacy Theses & Dissertations (2009 - 2024)

Wireless communications have become ubiquitous, enabling seamless connectivity and driv- ing innovations across various domains. As we look to the future, visible light communication (VLC) is a promising technology that offers the potential to revolutionize how we transmit and receive data. It seamlessly integrates multiple functionalities, including localization, control/sensing, and high-speed data transmission.This thesis proposes a multi-task learning deep convolutional neural network approach to optimize a hybrid waveform for VLC-enabled networks. By integrating Beacon Posi- tion Modulation (BPM), Beacon Phase Shift Keying (BPSK), and OFDM symbols within a virtual Pulse Width Modulation (PWM) envelope, this waveform supports localization, control/sensing, and …

Hardware Trojan Detection Utilizing Graph Neural Networks And Structural Checking, Hunter Nauman

Graduate Theses and Dissertations

The integrated circuit (IC) industry has experienced exponential growth, particularly in the complexity and scale of hardware designs. To sustain this growth, faster development cycles and cost-effective solutions have been the focus for many companies. One strategy to maintain this growth is through the incorporation of third-party intellectual property (IP) into the IC design process. Outsourcing the production of sub-components reduces development time and enables faster time-to-market, however, this approach also introduces the threat of Hardware Trojans. Hardware Trojans, defined as any malicious modification or addition to an IC, pose significant security risks due to their small size, low activation …

Database And Machine Learning Model For Classifying Autism Spectrum Disorder From Smartphone Based Electroretinography, Rory Harris

Honors Scholar Theses

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that negatively affects a patient’s cognitive and communication aptitude and, therefore, can severely impact that patient’s quality of life. Because of this, early diagnosis is paramount. In recent studies, electroretinography (ERG), which is a measure of the retina’s electrical response to a brief flash of light into the eye, has shown promise in detecting ASD. Access to these scans can provide early diagnosis, improving well-being. Current ERG devices are very expensive due to their on board processing capabilities. This paper aims to create an ERG device using a smartphone as the main …

Simulating And Training Autonomous Rover Navigation In Unity Engine Using Local Sensor Data, Christopher Pace

Senior Honors Theses

Autonomous navigation is essential to remotely operating mobile vehicles on Mars, as communication takes up to 20 minutes to travel between the Earth and Mars. Several autonomous navigation methods have been implemented in Mars rovers and other mobile robots, such as odometry or simultaneous localization and mapping (SLAM) until the past few years when deep reinforcement learning (DRL) emerged as a viable alternative. In this thesis, a simulation model for end-to-end DRL Mars rover autonomous navigation training was created using Unity Engine, using local inputs such as GNSS, LiDAR, and gyro. This model was then trained in navigation in a …

Design And Application Of Smart Systems To Address Analytical Problems, Lucas B. Ayres

All Dissertations

This dissertation is a multidisciplinary effort that integrates low-cost analytical instrumentation, redox chemistry, and artificial intelligence to overcome existing limitations in the fields of wearable sensing technology, Deep Eutectic Solvents (DES), and antioxidant chemistry. The overall goal behind each implemented strategy is to enhance the accuracy, efficiency, and accessibility of analytical processes and technologies. A general overview of the thesis, along with the research outcomes is included in Chapter One. The theoretical framework of this dissertation is presented in Chapter Two. Chapter Three describes the development of a wearable platform (sensor and instrumentation) to rapidly detect (~20 minutes) S. aureus …

Transferable User-Friendly Machine Learning For Normal Boiling Point Prediction, Frank Tafadzwa Mtetwa

Theses and Dissertations

The estimation of thermophysical properties of chemical compounds holds considerable importance across a multitude of fields, ranging from material science to process design. Among these properties, the normal boiling point (NBP) stands out as a pivotal parameter in scientific and engineering contexts, as it elucidates the state of a substance under standard conditions commonly encountered in nature. Additionally, NBP is extensively documented in various reference materials and databases. Given its paramount importance and widespread availability, prediction methodologies for other properties such as critical temperature, liquid density, vapor pressure, surface tension, liquid viscosity, liquid thermal conductivity, and flash point often rely …

Enhancing Cyber Resilience: Development, Challenges, And Strategic Insights In Cyber Security Report Websites Using Artificial Inteligence, Pooja Sharma

Harrisburg University Dissertations and Theses

In an era marked by relentless cyber threats, the imperative of robust cyber security measures cannot be overstated. This thesis embarks on an in-depth exploration of the historical trajectory and contemporary relevance of penetration testing methodologies, elucidating their evolution from nascent origins to indispensable tools in the cyber security arsenal. Moreover, it undertakes the ambitious task of conceptualizing and implementing a cyber security report website, meticulously designed to fortify cyber resilience in the face of ever-evolving threats in the digital realm.

The research journey commences with an insightful examination of the historical antecedents of penetration testing, tracing its genesis in …

Enhancement Of Rainfall Prediction And Satellite Precipitation Estimates: A Machine Learning Approach For United Arab Emirates, Faisal Baig

Dissertations

The accurate estimation of rainfall in arid regions presents a significant challenge due to sparse ground observations and complex atmospheric dynamics. This dissertation investigates various aspects of rainfall estimation and prediction in the United Arab Emirates (UAE), employing Satellite Precipitation Products (SPPs) and Machine Learning (ML) techniques. The research is structured around four key articles, each addressing different facets of the overarching theme. The first publication examines rainfall variability, consistency, and concentration in the UAE by comparing satellite precipitation products with rain gauge observations, highlighting the discrepancies between the two data sources. The second publication evaluates the performance of precipitation …

Enhancing Information Architecture With Machine Learning For Digital Media Platforms, Taylor N. Mietzner

Honors College Theses

Modern advancements in machine learning are transforming the technological landscape, including information architecture within user experience design. With the unparalleled amount of user data generated on online media platforms and applications, an adjustment in the design process to incorporate machine learning for categorizing the influx of semantic data while maintaining a user-centric structure is essential. Machine learning tools, such as the classification and recommendation system, need to be incorporated into the design for user experience and marketing success. There is a current gap between incorporating the backend modeling algorithms and the frontend information architecture system design together. The aim of …

Development Of Data-Driven Models To Predict Chemical Dosage And Energy Intensity For Different Supply Sources Of Drinking Water, Rehnuma Salsavil

USF Tampa Graduate Theses and Dissertations

As part of the UN Sustainable Development Goals (SDG6), access to clean water for the entire population and sustainable management of water resources have been stressed. Demand for water is increasing due to population growth, economic development, water quality degradation, and climate change. More water needs to be produced for the community at lower treatment costs. The chemical dosage and energy intensity are two important factors that influence water treatment costs and sustainable management of resources as well.

Previously, chemical dosage modeling to reduce treatment costs have been explored but there have been more focus in coagulant dosage modeling. Other …