Engineering Commons^™

Gps-Denied Localization Of Landing Evtol Aircraft, Aaron C. Brown

Theses and Dissertations

This thesis presents a dedicated GPS-denied landing system designed for electric vertical takeoff and landing (eVTOL) aircraft. The system employs active fiducial light pattern localization (AFLPL), which provides highly accurate and reliable navigation during critical landing phases. AFLPL utilizes images of a constellation comprised of modulating infrared lights strategically positioned on the landing site, to determine the aircraft pose through the use of a perspective-n-point (PnP) solver. The AFLPL system underwent thorough development, enhancement, and implementation to address and demonstrate its potential in navigation and its inherent limitations. A proposed method addresses the limitations of AFLPL by using an extended …

Advancing Winter Weather Adas: Tire Track Identification And Road Snow Coverage Estimation Using Deep Learning And Sensor Integration, Parth Kadav

Masters Theses

No abstract provided.

Automatic Cardiac Mri Image Segmentation And Mesh Generation, Ziyuan Li

McKelvey School of Engineering Theses & Dissertations

Segmenting and reconstructing cardiac anatomical structures from magnetic resonance (MR) images is essential for the quantitative measurement and automatic diagnosis of cardiovascular diseases [1]. However, manual evaluation of the time-series cardiac MRI (CMRI) obtained during routine clinical care are laborious, inefficient, and tends to produce biased and non-reproducible results [2]. This thesis proposes an end-to-end pipeline for automatically segmenting short-axis (SAX) CMRI images and generating high-quality 2D and 3D meshes suitable for finite element analysis. The main advantage of our approach is that it can not only work as a stand-alone pipeline for the automatic CMR image segmentation and mesh …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Learning To Rig Characters, Zhan Xu

Doctoral Dissertations

With the emergence of 3D virtual worlds, 3D social media, and massive online games, the need for diverse, high-quality, animation-ready characters and avatars is greater than ever. To animate characters, artists hand-craft articulation structures, such as animation skeletons and part deformers, which require significant amount of manual and laborious interaction with 2D/3D modeling interfaces. This thesis presents deep learning methods that are able to significantly automate the process of character rigging. First, the thesis introduces RigNet, a method capable of predicting an animation skeleton for an input static 3D shape in the form of a polygon mesh. The predicted skeletons …

Developing A Vision-Based Framework For Measuring And Monitoring Water Resource Systems Using Computer Vision And Deep Learning Techniques, Seyed Mohammad Hassan Erfani

Theses and Dissertations

Increased vulnerability of water systems to extreme events and climate change is among the profound challenges facing the management of water resource systems around the world. Extreme events, including droughts, floods, and natural hazards have become more frequent and intensive, particularly in coastal regions. Floods, for instance, caused tens of billions of US dollars losses and put the lives of thousands in danger, globally. To cope with the adverse consequences of floods, a wide range of structural, non-structural, and emergency measures are studied and deployed by flood management sectors. Various flood simulation, mapping, and forecast systems have been developed to …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi

Theses and Dissertations

The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the …

Deeptype: A Deep Neural Network Approach To Keyboard-Free Typing, Joshua V. Broekhuijsen

Theses and Dissertations

Textual data entry is an increasingly-important part of Human-Computer Interaction (HCI), but there is room for improvement in this domain. First, the keyboard -- a foundational text-entry device -- presents ergonomic challenges in terms of comfort and accuracy for even well-trained typists. Second, touch-screen smartphones -- some of the most ubiquitous mobile devices -- lack the physical space required to implement a full-size physical keyboard, and settle for a reduced input that can be slow and inaccurate. This thesis proposes and examines "DeepType" to begin addressing both of these problems in the form of a fully-virtual keyboard, realized through a …

Analyzing The Benthic Cover Of Crustose Coralline Algae Using Mask-R Cnn, Rachana Ravindra

Master's Projects

Coral reefs, supporting 25% of marine biodiversity, confront challenges from local and global impacts like overfishing, runoff, acidification, and warming. Crustose Coralline Algae (CCA), pivotal for reef structure and coral settlement, are underrepresented in research. Current methods like Coral Point Count with Excel Extensions (CPCe) have limitations, relying on image quality and being time-consuming. This paper proposes computer vision and Mask R-CNN, a supervised machine learning model, for CCA analysis in reef images, considering color, texture, and shape. Results indicate promise in clustering and classifying organisms. The innovative technology reduces manual labor, enhancing image analysis, simplifying the understanding of CCA’s …

A Computer Vision-Based Method For Tack Coat Coverage Inspection Using Drone-Collected Images, Aida Da Silva

Graduate Theses, Dissertations, and Problem Reports

Tack coat is a thin asphalt applied between the existing surface and asphalt overlay during road rehabilitation. The uniformity of tack coat coverage plays a vital role in providing adhesive bonding between the two layers in the pavement structures. To ensure tack coat uniformity, the current practice primarily relies on manual inspection during construction by field experts. This process is time-consuming and tedious, and the results can be subjective and error-prone. Drones have emerged as a non-destructive sensing technology in the construction industry for many inspection practices. Unlike other non-destructive inspection technologies, drones offer benefits ranging from accelerating data collection …

Machine Learning Assisted Framework For Advanced Subsurface Fracture Mapping And Well Interference Quantification, Mohammad Faiq Adenan

Graduate Theses, Dissertations, and Problem Reports

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., …

An Augmented Reality Maintenance Assistant With Real-Time Quality Inspection On Handheld Mobile Devices, James Thomas Frandsen

Theses and Dissertations

With the advances of industry 4.0, augmented reality (AR) devices are being deployed across the manufacturing sector to enhance worker perception and efficiency. AR is often used to deliver spatially relevant work instructions on mobile devices for maintenance procedures on the factory floor. In these situations, workers use their mobile devices to view instructions in the form of 3D animations and annotations that directly overlay the equipment being maintained. Workers then follow the AR instructions and must ultimately rely on their own judgement and knowledge of the procedure as they progress from step to step. An AR assistant that could …

Development Of A 3d-Printed Microfluidic Droplet-On-Demand System For The Deterministic Encapsulation And Processing Of Biological Materials, Chandler A. Warr

Theses and Dissertations

The growing threat of antimicrobial resistance is among the largest concerns in the world today. One method under development to combat this issue is the encapsulation of microbes in microfluidic droplets for single-cell testing. This method may be able to circumvent the need for a traditional positive cell culture which consumes the majority of the testing time using current diagnostic methods. This dissertation presents a method by which to deterministically encapsulate microbes using an artificial intelligence object detection algorithm and a Droplet-On-Demand microfluidic device. To accomplish this, the Droplet-On-Demand microfluidic device was first developed using a unique 3D-printing manufacturing method. …

Detection, Tracking, And Classification Of Aircraft And Birds From Multirotor Small Unmanned Aircraft Systems, Chester Valentine Dolph

Electrical & Computer Engineering Theses & Dissertations

The ability for small Unmanned Aircraft Systems (sUAS) to safely operate beyond visual line of sight (BVLOS) is of great interest to governments, businesses, and scientific research. One critical element for sUAS to operate BVLOS is the capability to avoid other air traffic. While many aircraft will be cooperative and broadcast their locations using Automatic Dependent Surveillance Broadcast (ADS-B), it is expected that many aircraft will remain non-cooperative – meaning they do not communicate position or flight plan to other aircraft. Avoiding mid-air collisions with non-cooperative aircraft is a critical limitation to widespread sUAS flying BVLOS. Examples of non-cooperative traffic …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque

Electrical & Computer Engineering Theses & Dissertations

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …

Automated Pre-Play Analysis Of American Football Formations Using Deep Learning, Jacob Deloy Newman

Theses and Dissertations

Annotation and analysis of sports videos is a time consuming task that, once automated, will provide benefits to coaches, players, and spectators. American football, as the most watched sport in the United States, could especially benefit from this automation. Manual annotation and analysis of recorded video of American football games is an inefficient and tedious process. Currently, most college football programs focus on annotating offensive formation. As a first step to further research for this unique application, we use computer vision and deep learning to analyze an overhead image of a football play immediately before the play begins. This analysis …

Automatic Testing Of Organic Strain Gauge Tactile Sensors., Brian P. Goulet

Electronic Theses and Dissertations

Human-Robot Interaction is a developing field of science, that is posed to augment everything we do in life. Skin sensors that can detect touch, temperature, distance, and other physical interaction parameters at the human-robot interface are very important to enhancing the collaboration between humans and machines. As such, these sensors must be efficiently tested and characterized to give accurate feedback from the sensor to the robot. The objective of this work is to create a diversified software testing suite that removes as much human intervention as possible. The tests and methodology discussed here provide multiple realistic scenarios that the sensors …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Heterogeneous Collaborative Mapping For Autonomous Mobile Systems, Sooraj Sunil

Electronic Theses and Dissertations

An accurate map of the environment is essential for autonomous robot navigation. During collaborative simultaneous localization and mapping, the individual robots usually represent the environment as probabilistic occupancy grid maps. These maps can be exchanged among robots and fused to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such fusion is challenging due to the unknown initial correspondence problem. This thesis presents a novel feature-based map fusion approach through detecting, describing, and matching geometrically consistent features present in the overlapping region between the maps. The main drawback of usual feature-based approaches is the incapability …

Image Geo-Localization With Cross-Attention, Connor Greenwell

Theses and Dissertations--Computer Science

The problem of estimating the location from which un-geotagged photographs were captured has been well studied by the computer vision community in recent years. The central proposal of this thesis is to define a common framework within which existing approaches can be constructed and evaluated, and to introduce a new method under this framework which uses cross-attention between the query image and a database of satellite imagery with known geotags. Our experiments fit within three broad categories: 1) evaluating the ability of image localization approaches to generalize to unseen regions; 2) examining performance changes under various reference database resolutions, scales, …

Building An Understanding Of Human Activities In First Person Video Using Fuzzy Inference, Bradley A. Schneider

Browse all Theses and Dissertations

Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by …

Ai-Driven Automated Medical Imaging Analysis, Jingya Liu

Dissertations and Theses

Medical imaging has been applied widely in many clinical diagnoses to detect and differentiate abnormalities by revealing the internal structure of the human body at normal anatomical and physiological levels. Manual analyzing medical images demands attention and is time-consuming, requiring well-trained expertise. The speed, fatigue, and experience may limit the diagnostic performance, leading to delays and even false diagnoses that significantly impact patient treatment. Therefore, accurate systematic systems based on medical image analysis are crucial for timely clinical diagnosis.

This dissertation focuses on advancing automatic computer-aided diagnosis systems to detect cancer, assisting radiologists with early intervention to improve survival rates. …

Lapnitor: A Web Service That Protects Your Laptop From Theft., Michael Ameteku

Williams Honors College, Honors Research Projects

Laptop theft is an issue worldwide. According to an article from 2018, Security Boulevard stated that a laptop is stolen every 53 seconds. Using a laptop's camera, we can monitor the surroundings of the laptop and reduce a laptop's probability of being stolen. According to the University of Pittsburgh, a laptop has a 1-in- 10 chance of being stolen and nearly half of these thefts occur in offices or classrooms. These thefts mostly occur when a laptop owner leaves their device unattended for a certain period of time to maybe go visit the restroom or attend to a call when …

Magic: The Gathering Card Virtualizer, Vincent Garbonick, Jacen C. Conlan, Jaret A. Varn

Williams Honors College, Honors Research Projects

Any well-versed Magic: The Gathering (MTG) player or collector knows how difficult it can be to keep track of all cards in their collection. Some spend hours searching for that one specific card, and others are constantly scouring the internet for how much their collection costs. However, this issue does not only affect casual fans. Resale companies spend hours a day determining the costs of cards, and tournament judges painstakingly check players’ decks to ensure they are not cheating. To assist with these struggles, the design team proposed to create the MTG Card Virtualizer. This device scans MTG playing cards …

Deep Learning For Automatic Microscopy Image Analysis, Shenghua He

McKelvey School of Engineering Theses & Dissertations

Microscopy imaging techniques allow for the creation of detailed images of cells (or nuclei) and have been widely employed for cell studies in biological research and disease diagnosis in clinic practices.Microscopy image analysis (MIA), with tasks of cell detection, cell classification, and cell counting, etc., can assist with the quantitative analysis of cells and provide useful information for a cellular-level understanding of biological activities and pathology. Manual MIA is tedious, time-consuming, prone to subject errors, and are not feasible for the high-throughput cell analysis process. Thus, automatic MIA methods can facilitate all kinds of biological studies and clinical tasks. Conventional …

Nonlinear Intelligent Model Predictive Control Of Mobile Robots, Benjamin Albia

Theses and Dissertations

This thesis presents a framework for an artificial neural network (ANN) model-based nonlinear model predictive control of mobile ground robots. A computer vision analysis module was first developed to extract quantitative position information from onboard camera feed with respect to a prescribed path. Various strategies were developed to construct nonlinear physical plant models for model predictive control (MPC), including the physics-based model (PBM), the ANN trained on PBM-generated data, the ANN trained on test-captured data, and the ANN initially trained on PBM-generated data and then retrained with captured data. All the models predict physical states and positions of the robot …

Visual Navigation And Control For Spacecraft Proximity Operations With Unknown Targets, Wyatt J. Harris

Theses and Dissertations

Many current and future spacecraft missions must conduct rendezvous and proximity operations (RPO) with resident space objects (RSOs). An important subset of spacecraft RPO that is yet to be demonstrated on-orbit involves final approach maneuvers with respect to RSOs where no information (such as geometry, inertia, relative velocity, etc.) is known about the target a priori, and no information is actively provided by the target during maneuvering. Such operation with respect to ‘unknown’ targets represents an important possible mission set for Department of Defense spacecraft and is the subject of this research. Two visual servoing frameworks capable of autonomously controlling …

Visual Cues For Semi-Autonomous Control Of Transradial Prosthetics, Mena S.A. Kamel

Electronic Thesis and Dissertation Repository

Upper-limb prosthetics are typically driven exclusively by biological signals, mainly electromyography (EMG), where electrodes are placed on the residual part of an amputated limb. In this approach, amputees must control each arm joint iteratively, in a proportional manner. Research has shown that sequential control of prosthetics usually imposes a cognitive burden on amputees, leading to high abandonment rates. This thesis presents a control system for upper-limb prosthetics, leveraging a computer vision module capable of simultaneously predicting objects in a scene, their segmentation mask, and a ranked list of the optimal grasping locations. The proposed system shares control with an amputee, …