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Articles 1 - 30 of 48
Full-Text Articles in Engineering
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa
Theses and Dissertations
Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …
Traffic Light Detection And V2i Communications Of An Autonomous Vehicle With The Traffic Light For An Effective Intersection Navigation Using Mavs Simulation, Mahfuzur Rahman
Theses and Dissertations
Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing …
Neural Networks For Improved Signal Source Enumeration And Localization With Unsteered Antenna Arrays, John T. Rogers Ii
Neural Networks For Improved Signal Source Enumeration And Localization With Unsteered Antenna Arrays, John T. Rogers Ii
Theses and Dissertations
Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case …
A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang
A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang
Theses and Dissertations
Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity …
Better Models For High-Stakes Tasks, Jacob Ryan Epifano
Better Models For High-Stakes Tasks, Jacob Ryan Epifano
Theses and Dissertations
The intersection of machine learning and healthcare has the potential to transform medical diagnosis, treatment, and research. Machine learning models can analyze vast amounts of medical data and identify patterns that may be too complex for human analysis. However, one of the major challenges in this field is building trust between users and the model. Due to things like high false alarm rate and the black box nature of machine learning models, patients and medical professionals need to understand how the model arrives at its recommendations. In this work, we present several methods that aim to improve machine learning models …
Vibration-Based Machine Learning Models For Condition Monitoring Of Railroad Rolling Stock, Sergio M. Martinez
Vibration-Based Machine Learning Models For Condition Monitoring Of Railroad Rolling Stock, Sergio M. Martinez
Theses and Dissertations
One of the primary causes of rail rolling stock derailments is attributed to bearing and wheel axle failures. The health of train bearings is primarily monitored at target locations through wayside detection systems. This practice is susceptible to bearing failure and potential derailments at points in between these wayside systems. To remedy this, the University Transportation Center for Railway Safety (UTCRS) has developed a wireless onboard monitoring system that can continuously monitor the vibration response, which directly correlates to the health of bearings. This data is used to train regression-based machine learning algorithms and long-term prediction neural networks to predict …
Development Of Atomistic Machine Learning Approaches For Thermal Properties Of Multi-Component Solids And Liquids, Alejandro David Rodriguez
Development Of Atomistic Machine Learning Approaches For Thermal Properties Of Multi-Component Solids And Liquids, Alejandro David Rodriguez
Theses and Dissertations
Currently, heat transfer in many industries is the limiting factor for innovation, especially in the energy sector. For example, maximizing thermal conductivity of ceramic coatings in power plant devices improves the overall electrical to thermal energy ratio, whereas minimizing thermal conductivity is required for desirable heat-to-electricity conversion in thermoelectric devices. As such, rapid discovery of new materials with extreme thermal conductivity values is quintessential for the near-future deployment of current and developing energy applications.
The vibrational properties of crystalline materials are essential for their ability to conduct heat. Fundamentally, the restorative atomic forces of displaced atoms are sufficient to represent …
Leveraging Programmable Switches To Enhance The Performance Of Networks: Active And Passive Deployments, Elie Kfoury
Leveraging Programmable Switches To Enhance The Performance Of Networks: Active And Passive Deployments, Elie Kfoury
Theses and Dissertations
The performance of networks today is drastically affected by: 1) switches equipped with large buffers, referred to as “bloated buffers”: due to the lack of programmability and traffic visibility in legacy switches, operators nowadays configure large buffers statically without considering the characteristics or dynamics of flows. Such buffers increase the delays on packets, causing the Quality of Service (QoS) of networked applications (e.g., voice over IP, web browsing) to degrade; 2) switches forwarding packets on a best-effort basis: traffic crossing a switch is heterogeneous in many ways. Mixing such traffic in a single queue without any QoS measures can drastically …
Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis
Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis
Theses and Dissertations
The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.
In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …
Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song
Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song
Theses and Dissertations
Discovering new materials and understanding their crystal structures and chemical properties are critical tasks in the material sciences. Although computational methodologies such as Density Functional Theory (DFT), provide a convenient means for calculating certain properties of materials or predicting crystal structures when combined with search algorithms, DFT is computationally too demanding for structure prediction and property calculation for most material families, especially for those materials with a large number of atoms. This dissertation aims to address this limitation by developing novel deep learning and machine learning algorithms for effective prediction of material crystal structures and properties. Our data-driven machine learning …
Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest
Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest
Theses and Dissertations
This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.
Applied Machine Learning In Development Of Geospatial Information Tools For Sustainable Groundwater Management, Saul Gallegos Ramirez
Applied Machine Learning In Development Of Geospatial Information Tools For Sustainable Groundwater Management, Saul Gallegos Ramirez
Theses and Dissertations
Groundwater plays an important role in sustainable water resource management. Globally, the development of groundwater resources for irrigation provides a stable water source and enhances food security. However, developing groundwater resources is difficult, requiring the collection, synthesis, analysis, and dissemination of information about groundwater in an accessible, effective manner so decision makers have the knowledge and tools to implement sustainable management strategies. In this dissertation, I present solutions to assist with the paucity of groundwater data. The first problem I explore is extending the Palmer Drought Severity Index to near present day. This dataset is crucial to current groundwater imputation …
Image-Based Cancer Diagnosis Using Novel Deep Neural Networks, Hosein Barzekar
Image-Based Cancer Diagnosis Using Novel Deep Neural Networks, Hosein Barzekar
Theses and Dissertations
Cancer is the major cause of death in many nations. This serious illness can only be effectivelytreated if it is diagnosed early. In contrast, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by modest modifications in individual cells or tissues, making them difficult to detect visually. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial expense, making the classification of …
Detection Of Rotorcraft Landing Sites: An Ai-Based Approach, Abdullah Nasir
Detection Of Rotorcraft Landing Sites: An Ai-Based Approach, Abdullah Nasir
Theses and Dissertations
The updated information about the location and type of rotorcraft landing sites is an essential asset for the Federal Aviation Administration (FAA) and the Department of Transportation (DOT). However, acquiring, verifying, and regularly updating information about landing sites is not straightforward. The lack of current and correct information about landing sites is a risk factor in several rotorcraft accidents and incidents. The current FAA database of rotorcraft landing sites contains inaccurate and missing entries due to the manual updating process. There is a need for an accurate and automated validation tool to identify landing sites from satellite imagery. This thesis …
Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel
Development Of Software Tools For Efficient And Sustainable Process Development And Improvement, Jake P. Stengel
Theses and Dissertations
Infrastructure is a key component in the well-being of our society that leads to its growth, development, and productive operations. A well-built infrastructure allows the community to be more competitive and promotes economic advancement. In 2021, the ASCE (American Society of Civil Engineers) ranked the American infrastructure as substandard, with an overall grade of C-. The overall ranking suffers when key infrastructure categories are not maintained according to the needs of the population. Therefore, there is a need to consider alternative methods to improve our infrastructure and make it more sustainable to enhance the overall grade. One of the challenges …
An Analysis Of Groundwater Storage Loss In The Central Valley Using A Novel In Situ Method Compared To Grace-Derived Results, Michael David Stevens
An Analysis Of Groundwater Storage Loss In The Central Valley Using A Novel In Situ Method Compared To Grace-Derived Results, Michael David Stevens
Theses and Dissertations
Robust groundwater management is necessary to maintain long-term aquifer sustainability. Temporally and spatially inconsistent in situ data prevents robust groundwater resource evaluation. Data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission has been used to evaluate long-term, large-scale groundwater trends. However, the spatial resolution of GRACE data presents challenges for groundwater management in medium-sized aquifers like the Central Valley of California (CV). Other researchers have utilized GRACEderived data to evaluate groundwater storage in the CV, but they often make corrections due to what is referred to as the "leakage effect." We demonstrate a method for imputing gaps in …
Deep Learning Based Generative Materials Design, Yong Zhao
Deep Learning Based Generative Materials Design, Yong Zhao
Theses and Dissertations
Discovery of novel functional materials is playing an increasingly important role in many key industries such as lithium batteries for electric vehicles and cell phones. However experimental tinkering of existing materials or Density Functional Theory (DFT) based screening of known crystal structures, two of the major current materials design approaches, are both severely constrained by the limited scale (around 250,000 in ICSD database) and diversity of existing materials and the lack of a sufficient number of materials with annotated properties. How to generate a large number of physically feasible, stable, and synthesizable crystal materials and build accurate property prediction models …
Project Leanness Score: A Machine Learning Approach, Julia Said
Project Leanness Score: A Machine Learning Approach, Julia Said
Theses and Dissertations
The construction industry is known to have several inadequacies in resource utilization leading to cost and schedule overruns. One of the popular recent methods that attempts to eliminate these inadequacies is lean construction principles, techniques and tools. Lean construction is a philosophy, backed with principles and tools, aiming at maximizing value, eliminating waste, optimizing efficiency, and seeking continuous improvement. Lean construction techniques (such as pull planning, just-in-time delivery, fail safe for quality, etc.) are widely researched and well developed. However, their implementation in construction sites is tricky as their success depends on several other factors such as the level of …
Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel
Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel
Theses and Dissertations
This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …
Machine Learning (Ml) - Assisted Tools For Enhancing Security And Privacy Of Edge Devices, Santosh Kumar Nukavarapu
Machine Learning (Ml) - Assisted Tools For Enhancing Security And Privacy Of Edge Devices, Santosh Kumar Nukavarapu
Theses and Dissertations
The rapid growth of edge-based IoT devices, their use cases, and autonomous communication has created new challenges with privacy and security. Side-channel attacks are one of the examples of security and privacy vulnerabilities that can cause inference at Internet-Service Provider (ISP) and local Wi-Fi networks. Such an attack would leak user’s sensitive information such as home occupancy, medical activity, and daily routines. Another example is that these devices have weak authentication and low encryption standards, making them an easy target for malware-based attacks such as denial of service or launching other network attacks using these infected devices. This thesis dissertation …
Smart City Management Using Machine Learning Techniques, Mostafa Zaman
Smart City Management Using Machine Learning Techniques, Mostafa Zaman
Theses and Dissertations
In response to the growing urban population, "smart cities" are designed to improve people's quality of life by implementing cutting-edge technologies. The concept of a "smart city" refers to an effort to enhance a city's residents' economic and environmental well-being via implementing a centralized management system. With the use of sensors and actuators, smart cities can collect massive amounts of data, which can improve people's quality of life and design cities' services. Although smart cities contain vast amounts of data, only a percentage is used due to the noise and variety of the data sources. Information and communication technology (ICT) …
Advanced Analytics In Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms And Parallel Machine Scheduling Using A Genetic Algorithm, Meiling He
Theses and Dissertations
Industry 4.0 offers great opportunities to utilize advanced data processing tools by generating Big Data from a more connected and efficient data collection system. Making good use of data processing technologies, such as machine learning and optimization algorithms, will significantly contribute to better quality control, automation, and job scheduling in Smart Manufacturing. This research aims to develop a new machine learning algorithm for solving highly imbalanced data processing problems, implement both supervised and unsupervised machine learning auto-selection frameworks for detecting anomalies in smart manufacturing, and develop a genetic algorithm for optimizing job schedules on unrelated parallel machines. This research also …
A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta
A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta
Theses and Dissertations
Many algorithms and methods have been proposed for inverse image processing applications, such as super-resolution, image de-noising, and image reconstruction, particularly with the recent surge of interest in machine learning and deep learning methods.
As for Computed Tomography (CT) image reconstruction, the most recently proposed methods are limited to image domain processing, where deep learning is used to learn the mapping between a true image data set and a noisy image data set in the image domain. While deep learning-based methods can produce higher quality images than conventional model-based algorithms, these methods have a limitation. Deep learning-based methods used in …
Medical Image Segmentation Using Machine Learning, Masoud Khani
Medical Image Segmentation Using Machine Learning, Masoud Khani
Theses and Dissertations
Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise …
Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification, Sarah Youssef
Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification, Sarah Youssef
Theses and Dissertations
Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the confidence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market efficiency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The benefit of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price …
Five Degree-Of-Freedom Property Interpolation Of Arbitrary Grain Boundaries Via Voronoi Fundamental Zone Octonion Framework, Sterling Gregory Baird
Five Degree-Of-Freedom Property Interpolation Of Arbitrary Grain Boundaries Via Voronoi Fundamental Zone Octonion Framework, Sterling Gregory Baird
Theses and Dissertations
In this work we introduce the Voronoi fundamental zone octonion (VFZO) interpolation framework for grain boundary (GB) structure-property models and surrogates. The VFZO framework offers an advantage over other five degree-of-freedom (5DOF) based property interpolation methods because it is constructed as a point set in a Riemannian manifold. This means that directly computed Euclidean distances approximate the original octonion distance with significantly reduced computation runtime (∼7 CPU minutes vs. 153 CPU days for a 50000×50000 pairwise-distance matrix). This increased efficiency facilitates lower interpolation error through the use of significantly more input data. We demonstrate grain boundary energy (GBE) interpolation results …
Deep Reinforcement Learning Applied To Spacecraft Attitude Control And Moment Of Inertia Estimation Via Recurrent Neural Networks, Nathaniel A. Enders
Deep Reinforcement Learning Applied To Spacecraft Attitude Control And Moment Of Inertia Estimation Via Recurrent Neural Networks, Nathaniel A. Enders
Theses and Dissertations
This study investigated two distinct problems related to unknown spacecraft inertia. The first problem explored the use of a recurrent neural network to estimate spacecraft moments of inertia using angular velocity measurements. Initial results showed that, for the configuration examined, the neural network can estimate the moments of inertia when there is a known external torque. The second problem trained a reinforcement learning agent, via proximal policy optimization, to control the attitude of a spacecraft. The results demonstrated that reinforcement learning may be a viable option for guidance and control solutions where the spacecraft model may be unknown. The trained …
Reinforcement Learning-Based Access Schemes In Cognitive Radio Networks, Ehab Maged Elguindy
Reinforcement Learning-Based Access Schemes In Cognitive Radio Networks, Ehab Maged Elguindy
Theses and Dissertations
In this thesis, we propose different MAC protocols based on three Reinforcement Learning (RL) approaches, namely Q-Learning, Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG). We exploit the primary user (PU) feedback, in the form of ARQ and CQI bits, to enhance the performance of the secondary user (SU) MAC protocols. Exploiting the PU feedback information can be applied on the top of any SU sensing-based MAC protocol. Our proposed model relies on two main pillars, namely, an infinite-state Partially Observable Markov Decision Process (POMDP) to model the system dynamics besides a queuing-theoretic model for the PU queue; the …
Estimating Affective States In Virtual Reality Environments Using The Electroencephalogram, Meghan R. Kumar
Estimating Affective States In Virtual Reality Environments Using The Electroencephalogram, Meghan R. Kumar
Theses and Dissertations
Recent interest in high-performance virtual reality (VR) headsets has motivated research efforts to increase the user's sense of immersion via feedback of physiological measures. This work presents the use of electroencephalographic (EEG) measurements during observation of immersive VR videos to estimate the user's affective state. The EEG of 30 participants were recorded as each passively viewed a series of one minute immersive VR video clips and subjectively rated their level of valence, arousal, dominance, and liking. Correlates between EEG spectral bands and the subjective ratings were analyzed to identify statistically significant frequencies and electrode locations across participants. Model feasibility and …
Computational Analysis And Prediction Of Intrinsic Disorder And Intrinsic Disorder Functions In Proteins, Akila I. Katuwawala
Computational Analysis And Prediction Of Intrinsic Disorder And Intrinsic Disorder Functions In Proteins, Akila I. Katuwawala
Theses and Dissertations
COMPUTATIONAL ANALYSIS AND PREDICTION OF INTRINSIC DISORDER AND INTRINSIC DISORDER FUNCTIONS IN PROTEINS
By Akila Imesha Katuwawala
A dissertation submitted in partial fulfillment of the requirements for the degree of Engineering, Doctor of Philosophy with a concentration in Computer Science at Virginia Commonwealth University.
Virginia Commonwealth University, 2021
Director: Lukasz Kurgan, Professor, Department of Computer Science
Proteins, as a fundamental class of biomolecules, have been studied from various perspectives over the past two centuries. The traditional notion is that proteins require fixed and stable three-dimensional structures to carry out biological functions. However, there is mounting evidence regarding a “special” class …