Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Machine learning (15)
- Deep learning (9)
- Machine Learning (8)
- Artificial intelligence (6)
- Deep Learning (5)
-
- Natural language processing (5)
- Augmented reality (4)
- Computer vision (4)
- Semantic segmentation (4)
- #afcec (3)
- Artificial neural networks (3)
- Network science (3)
- Neural networks (3)
- Virtual reality (3)
- Aerial refueling (2)
- Augmented Reality (2)
- BERT (2)
- CNN (2)
- Computational science (2)
- Convolutional Neural Network (2)
- Convolutional neural network (2)
- Cybersecurity (2)
- Genetic algorithms (2)
- Internet of Things (2)
- Network protocols (2)
- Neural network (2)
- Optimization (2)
- Quantum computing (2)
- Self-assembly (2)
- 2-handed assembly model (1)
Articles 1 - 30 of 124
Full-Text Articles in Entire DC Network
Methods For Drone Trajectory Analysis Of Bottlenose Dolphins (Tursiops Truncatus), Jillian D. Bliss
Methods For Drone Trajectory Analysis Of Bottlenose Dolphins (Tursiops Truncatus), Jillian D. Bliss
Theses and Dissertations
With the increase in the use of UAS (Unmanned Aerial Systems) for marine mammal research, there is a need for the development of methods of analysis to transform UAS high resolution video into quantitative data. This study sought to develop a preliminary method of analysis that would quantify and present a way to visualize the dynamics and relative spatial distribution and changes in distribution of bottlenose dolphins (Tursiops truncatus) in the waters of Turneffe Atoll, Belize. This approach employs a previously developed video tracking program ‘Keypoint Tracking’ that enables manual tracking of individual dolphins and the creation of …
Assessing Wood Failure In Plywood By Deep Learning/Semantic Segmentation, Ramon Ferreira Oliveira
Assessing Wood Failure In Plywood By Deep Learning/Semantic Segmentation, Ramon Ferreira Oliveira
Theses and Dissertations
The current method for estimating wood failure is highly subjective. Various techniques have been proposed to improve the current protocol, but none have succeeded. This research aims to use deep learning/semantic segmentation using SegNet architecture to estimate wood failure in four types of three-ply plywood from mechanical shear strength specimens. We trained and tested our approach on custom and commercial plywood with bio-based and phenol-formaldehyde adhesives. Shear specimens were prepared and tested. Photographs of 255 shear bonded areas were taken. Forty photographs were used to solicit visual estimates from five human evaluators, and the remaining photographs were used to train …
Augmented Reality Fonts With Enhanced Out-Of-Focus Text Legibility, Mohammed Safayet Arefin
Augmented Reality Fonts With Enhanced Out-Of-Focus Text Legibility, Mohammed Safayet Arefin
Theses and Dissertations
In augmented reality, information is often distributed between real and virtual contexts, and often appears at different distances from the viewer. This raises the issues of (1) context switching, when attention is switched between real and virtual contexts, (2) focal distance switching, when the eye accommodates to see information in sharp focus at a new distance, and (3) transient focal blur, when information is seen out of focus, during the time interval of focal distance switching. This dissertation research has quantified the impact of context switching, focal distance switching, and transient focal blur on human performance and eye fatigue in …
An Ethercat Based Real-Time Control System Design For Wheelchair-Mounted 6dof Assistive Robotic Arm, Ivan Alexander Rulik Cote
An Ethercat Based Real-Time Control System Design For Wheelchair-Mounted 6dof Assistive Robotic Arm, Ivan Alexander Rulik Cote
Theses and Dissertations
Numerous assistive robots for individuals with disabilities have been produced over the past ten years, but researchers have not completely exploited these robotic technologies to enable people with impairments to live independently, especially in respect to activities of daily living. (ADLs). For people with impairments, an assistive system can help them fulfill the requirements of typical ADLs. Assistive robots can help address future healthcare demands due to a growing need for caregivers, a scarcity of them, and an increase in the number of the elderly and people with disabilities. Enhancing functional independence while creating a superior human-machine interaction is one …
Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed
Theses and Dissertations
Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …
A Protocol To Build Trust With Black Box Models, Timothy K. Thielke
A Protocol To Build Trust With Black Box Models, Timothy K. Thielke
Theses and Dissertations
Data scientists are more widely using artificial intelligence and machine learning (ML) algorithms today despite the general mistrust associated with them due to the lack of contextual understanding of the domain occurring within the algorithm. Of the many types of ML algorithms, those that use non-linear activation functions are especially regarded with suspicion because of the lack of transparency and intuitive understanding of what is occurring within the black box of the algorithm. In this thesis, we set out to create a protocol to delve into the black box of an ML algorithm set to predict synoptic severe weather patterns …
Atomlbs: An Atom Based Convolutional Neural Network For Druggable Ligand Binding Site Prediction, Md Ashraful Islam
Atomlbs: An Atom Based Convolutional Neural Network For Druggable Ligand Binding Site Prediction, Md Ashraful Islam
Theses and Dissertations
Despite advances in drug research and development, there are few and ineffective treatments for a variety of diseases. Virtual screening can drastically reduce costs and accelerate the drug discovery process. Binding site identification is one of the initial and most important steps in structure-based virtual screening. Identifying and defining protein cavities that are likely to bind to a small compound is the objective of this task. In this research, we propose four different convolutional neural networks for predicting ligand-binding sites in proteins. A parallel optimized data pipeline is created to enable faster training of these neural network models on minimal …
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 …
Design Of Ethical Autonomous Agents For Unmanned Aerial Vehicles Using Fuzzy Logic, Gavin Giovanni Smith
Design Of Ethical Autonomous Agents For Unmanned Aerial Vehicles Using Fuzzy Logic, Gavin Giovanni Smith
Theses and Dissertations
Autonomous systems have, over the years become part of our everyday lives. These systems have been deployed to executed a diverse range of applications in different industries; finance, healthcare, military, and in particular, the flight industry. With the rise of UAVs, new opportunities arose, but with those opportunities came new pitfalls within any industry. For UAVs, one of the pitfalls came in the form of ethical decisionmaking, which led to a variety of questions. Can the Autonomous systems within UAVs be designed with ethics in mind? Which ethical guidelines would we use to implement such a system? How would we …
Evaluating Deep Learning Explanations On Risc-V Assembly As A Reverse Engineering Aid, Daniel F. Koranek
Evaluating Deep Learning Explanations On Risc-V Assembly As A Reverse Engineering Aid, Daniel F. Koranek
Theses and Dissertations
This dissertation addresses several problems surrounding the detection of malware using deep learning models trained on assembly language examples. First, it examines the feasibility of detecting examples of malice using deep learning models trained on RISC-V instruction traces. Next, it examines whether models for detecting trace features and code features in RISC-V assembly can be made explainable (providing rationale for a model’s decision based upon the model’s internal workings) or interpretable (providing additional rationale as model output to support a human’s agreement with the model output). Third, this work examines ways in which it is possible to give additional contextual …
Non-Negative Matrix Factorization In The Identification Of Co-Mutations, Michael Robert Kolar
Non-Negative Matrix Factorization In The Identification Of Co-Mutations, Michael Robert Kolar
Theses and Dissertations
One of the difficulties of genetic research is the asymmetrical relationship between data collection techniques and data analysis techniques. The goal of this research was to test a novel application of non-negative matrix factorization, which would allow researchers to more easily identify co-mutations. Those co-mutations then can then be further verified by frequency analysis. This pruning process allows researchers to identify more fruitful research opportunities, saving time, energy, and funding. Past research has utilized non-negative matrix factorization to extract factors which meaningfully express underlying data features. This study extends the depth of non-negative matrix factorization knowledge in various ways. First, …
Representation Learning For Open Set Recognition And Novel Category Discovery, Jingyun Jia
Representation Learning For Open Set Recognition And Novel Category Discovery, Jingyun Jia
Theses and Dissertations
As machine learning models have achieved great success in various research and industry fields, the success of these models heavily relies on the massive amount of data collection and human annotations. While the real world is an open set, the daily emerged categories and the lacking of annotations have become new challenges for machine learning models. The absence of newly emerged categories in training samples can be captured by Open Set Recognition (OSR). Then, given the newly emerged samples, the process of automatically identifying the novel categories is called Novel Category Discovery (NCD). In this dissertation, we focused on learning …
Utilizing Federated Learning And Meta Learning For Few-Shot Learning On Edge Devices, Kousalya Soumya Lahari Voleti
Utilizing Federated Learning And Meta Learning For Few-Shot Learning On Edge Devices, Kousalya Soumya Lahari Voleti
Theses and Dissertations
The efficient and effective handling of few-shot learning tasks on mobile devices is challenging due to the small training set issue and the physical limitations in power and computational resources on these devices. In this thesis, we propose a solution that combines federated learning and meta-learning to handle independent few-shot learning tasks on multiple devices (or clients) and the server. In particular, we utilize the Prototypical Networks to perform meta-learning on all devices to learn multiple independent few-shot learning models and to combine the models in a centralized data distributed architecture using federated learning which can be reused by the …
Cnn-Based Dendrite Core Detection From Microscopic Images Of Directionally Solidified Ni-Base Alloys, Xiaoguang Li
Cnn-Based Dendrite Core Detection From Microscopic Images Of Directionally Solidified Ni-Base Alloys, Xiaoguang Li
Theses and Dissertations
Dendrite core is the center point of the dendrite. The information of dendrite core is very helpful for material scientists to analyze the properties of materials. Therefore, detecting the dendrite core is a very important task in the material science field. Meanwhile, because of some special properties of the dendrites, this task is also very challenging. Different from the typical detection problems in the computer vision field, detecting the dendrite core aims to detect a single point location instead of the bounding-box. As a result, the existing regressing bounding-box based detection methods can not work well on this task because …
Empirical Studies On Automated Software Testing Practices, Alireza Salahirad
Empirical Studies On Automated Software Testing Practices, Alireza Salahirad
Theses and Dissertations
Software testing is notoriously difficult and expensive, and improper testing carries economic, legal, and even environmental or medical risks. Research in software testing is critical to enabling the development of the robust software that our society relies upon. This dissertation aims to lower the cost of software testing without decreasing the quality by focusing on the use of automation. The dissertation consists of three empirical studies on aspects of software testing. Specifically, these three projects focus on (1) mapping the connections between research topics and the evolution of research topics in the field of software testing, (2) an assessment of …
Human Activity Recognition (Har) Using Wearable Sensors And Machine Learning, Chrisogonas Odero Odhiambo
Human Activity Recognition (Har) Using Wearable Sensors And Machine Learning, Chrisogonas Odero Odhiambo
Theses and Dissertations
Humans engage in a wide range of simple and complex activities. Human Activity Recognition (HAR) is typically a classification problem in computer vision and pattern recognition, to recognize various human activities. Recent technological advancements, the miniaturization of electronic devices, and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments, alongside smart wearable sensors, have opened the door to numerous opportunities for adding value and personalized services to citizens. Vision-based and sensory-based HAR find diverse applications in healthcare, surveillance, sports, event analysis, Human-Computer …
Applications Of Machine Learning For Improved Patient Selection And Therapy Recommendations, Brendan Elochukwu Odigwe
Applications Of Machine Learning For Improved Patient Selection And Therapy Recommendations, Brendan Elochukwu Odigwe
Theses and Dissertations
The public health domain continues to battle with illness and the growing need for continuous advancement in our approach to clinical care. Individuals experiencing certain conditions undergo tried and tested therapies and medications, practices that have become the mainstay and standard of care in clinical medicine. As with all therapies and medications, they don't always work the same way and do not work for everyone. Some Treatment regimens, like Hydroxyurea medication, which is commonly administered to Sickle cell anemia patients, come with some adverse side effects due to the chemotherapeutic nature of the drug. This would be particularly disappointing if …
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
Theses and Dissertations
Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …
Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo
Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo
Theses and Dissertations
Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a …
Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho
Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho
Theses and Dissertations
We develop new machine learning and statistical methods that are tailored for Air and Space applications through the incorporation of subject matter expertise. In particular, we focus on three separate research thrusts that each represents a different type of subject matter knowledge, modeling approach, and application. In our first thrust, we incorporate knowledge of natural phenomena to design a neural network algorithm for localizing point defects in transmission electron microscopy (TEM) images of crystalline materials. In our second research thrust, we use Bayesian feature selection and regression to analyze the relationship between fighter pilot attributes and flight mishap rates. We …
Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon
Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon
Theses and Dissertations
The threat of eavesdropping, and the challenge of recognizing and correcting for corrupted or suppressed information in communication systems is a consistent challenge. Effectively managing protection mechanisms requires an ability to accurately gauge the likelihood or severity of a threat, and adapt the security features available in a system to mitigate the threat. This research focuses on the design and development of a security-focused communication protocol at the session-layer based on a re-prioritized communication architecture model and associated metrics. From a probabilistic model that considers data leakage and data corruption as surrogates for breaches of confidentiality and integrity, a set …
Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero
Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero
Theses and Dissertations
Quantum computers are beset by errors from a variety of sources. Although quantum error correction and detection codes have been developed since the 1990s, these codes require mid-circuit measurements in order to operate. In order to avoid these measurements we have developed a new error detection code that only requires state collapses at the end of the circuit, which we call no ancilla error detection (NAED). We investigate some of the mathematics behind NAED such as which codes can detect which errors. We then run NAED on three separate types of circuits: Greenberger–Horne–Zeilinger circuits, phase dependent circuits, and a quantum …
Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone
Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone
Theses and Dissertations
This research is intended to determine which styles of layout algorithm are well suited to Command and Control (C2) network graphs to replace current manual layout methods. Manual methods are time intensive and an automated layout algorithm should decrease the time spent creating network graphs. Simulations on realistic synthetically generated graphs provide information to help infer which algorithms perform better than others on this problem. Data is generated using statistics drawn from multiple real world C2 network graphs. The three algorithms tested against this data are the Spectral algorithm, the Dot algorithm, and the Fruchterman-Reingold algorithm. The results include a …
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Theses and Dissertations
Analysis of cyber-physical systems (CPS) has emerged as a critical domain for providing US Air Force and Space Force leadership decision advantage in air, space, and cyberspace. Legacy methods have been outpaced by evolving battlespaces and global peer-level challengers. Automation provides one way to decrease the time that analysis currently takes. This thesis presents an event detection automation system (EDAS) which utilizes deep learning models, distance metrics, and static thresholding to detect events. The EDAS automation is evaluated with case study of CPS domain experts in two parts. Part 1 uses the current methods for CPS analysis with a qualitative …
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Theses and Dissertations
Cyberspace is the digital communications network that supports the internet of battlefield things (IoBT), the model by which defense-centric sensors, computers, actuators and humans are digitally connected. A secure IoBT infrastructure facilitates real time implementation of the observe, orient, decide, act (OODA) loop across distributed subsystems. Successful hacking efforts by cyber criminals and strategic adversaries suggest that cyber systems such as the IoBT are not secure. Three lines of effort demonstrate a path towards a more robust IoBT. First, a baseline data set of enterprise cyber network traffic was collected and modelled with generative methods allowing the generation of realistic, …
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Theses and Dissertations
Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …
Effective Immersive Analytics For Everyday Use, Benjamin D. Weidner
Effective Immersive Analytics For Everyday Use, Benjamin D. Weidner
Theses and Dissertations
Data visualization is an important field of work that takes in uncountable amounts of indexes to create an easy-to-read interpretation of what was previously unreadable. Immersive analytics is the new field that brings 3D data visualization to virtual reality, immersing users directly into the data. Focusing on bringing humans and computers closer together through natural function can benefit the world of data science. In order to accurately utilize this field to benefit this world, principles must be laid out and observed to see which techniques and methods are best fit for an everyday immersive analytics platform. Our findings show that, …
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn
Theses and Dissertations
With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …
Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt
Gpgpu Microbenchmarking For Irregular Application Optimization, Dalton R. Winans-Pruitt
Theses and Dissertations
Irregular applications, such as unstructured mesh operations, do not easily map onto the typical GPU programming paradigms endorsed by GPU manufacturers, which mostly focus on maximizing concurrency for latency hiding. In this work, we show how alternative techniques focused on latency amortization can be used to control overall latency while requiring less concurrency. We used a custom-built microbenchmarking framework to test several GPU kernels and show how the GPU behaves under relevant workloads. We demonstrate that coalescing is not required for efficacious performance; an uncoalesced access pattern can achieve high bandwidth - even over 80% of the theoretical global memory …
X-Ray Vision At Action Space Distances: Depth Perception In Context, Nate Phillips
X-Ray Vision At Action Space Distances: Depth Perception In Context, Nate Phillips
Theses and Dissertations
Accurate and usable x-ray vision has long been a goal in augmented reality (AR) research and development. X-ray vision, or the ability to comprehend location and object information when such is viewed through an opaque barrier, would be imminently useful in a variety of contexts, including industrial, disaster reconnaissance, and tactical applications. In order for x-ray vision to be a useful tool for many of these applications, it would need to extend operators’ perceptual awareness of the task or environment. The effectiveness with which x-ray vision can do this is of significant research interest and is a determinant of …