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Full-Text Articles in Engineering

Augmented Reality Headset Facilitates Exposure For Surgical Stabilization Of Rib Fractures, T. Sensing, Pratik Parikh, Claire Hardman, Thomas Wischgoll, Sadan Suneesh Menon Jan 2021

Augmented Reality Headset Facilitates Exposure For Surgical Stabilization Of Rib Fractures, T. Sensing, Pratik Parikh, Claire Hardman, Thomas Wischgoll, Sadan Suneesh Menon

Computer Science and Engineering Faculty Publications

Recent advances in augmented reality (AR) technology have made it more accessible, portable, and powerful. AR headsets differentiate themselves from virtual reality in that they allow the wearer an unobstructed view of the “real world” but with an image superimposed upon it. The technology has many potential applications in medicine, including surgical planning, simulation, and medical education. The aim of this project was to provide proof of concept that using an AR headset during surgical stabilization of rib fractures (SSRF) is feasible. We theorized that the use of AR could allow for more precise localization of fractures, allowing for smaller ...


Multi-Echo Quantitative Susceptibility Mapping For Strategically Acquired Gradient Echo (Stage) Imaging, Sara Gharabaghi, Saifeng Liu, Ying Wang, Yongsheng Chen, Sagar Buch, Mojtaba Jokar, Thomas Wischgoll, Nasser H. Kashou, Chunyan Zhang, Bo Wu, Jingliang Cheng, E. Mark Haacke Oct 2020

Multi-Echo Quantitative Susceptibility Mapping For Strategically Acquired Gradient Echo (Stage) Imaging, Sara Gharabaghi, Saifeng Liu, Ying Wang, Yongsheng Chen, Sagar Buch, Mojtaba Jokar, Thomas Wischgoll, Nasser H. Kashou, Chunyan Zhang, Bo Wu, Jingliang Cheng, E. Mark Haacke

Computer Science and Engineering Faculty Publications

Purpose: To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. Methods: The proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The ...


Additive Manufacturing And How 3d Printing Is Fighting Covid-19, Raghavan Srinivasan, Ahsan Mian, Joy Gockel, Laura M. Luehrmann May 2020

Additive Manufacturing And How 3d Printing Is Fighting Covid-19, Raghavan Srinivasan, Ahsan Mian, Joy Gockel, Laura M. Luehrmann

Mechanical and Materials Engineering Faculty Publications

This is the fourth installment in the Shelter in Place (SiP) Lecture series. This installment covers the creative ways that 3D printing has supported efforts to combat the pandemic. It covers the basics of what 3D printing is, some of the various creative projects that have used 3D printing to combat the pandemic, among other topics and questions by the pandemic.


Measuring Nomophobia And Exploration Of Consequences And Comorbidities, Sarah Marie Fryman, William L. Romine Apr 2020

Measuring Nomophobia And Exploration Of Consequences And Comorbidities, Sarah Marie Fryman, William L. Romine

Symposium of Student Research, Scholarship, and Creative Activities Materials

Excessive use of smartphones has coined the term “Nomophobia”, or fear of not being able to use your smartphone. For many, these devices have become an extension of ourselves, which raises hesitation on whether or not society has become addicted to smartphones. Specific diagnostic criteria for smartphone addiction have yet to be settled, and even appropriate to use the word “addiction” when describing excessive usage of smartphones is controversial.

We therefore explore utilize current measures to explore the symptoms of nomophobia and their hierarchy, as well as comorbidities including social anxiety, self-esteem, distracted driving and sleep quality. A total of ...


Predicting Alcohol Withdrawal In Intensive Care Units, Reza Sadeghi, Tanvi Banerjee, William L. Romine Apr 2020

Predicting Alcohol Withdrawal In Intensive Care Units, Reza Sadeghi, Tanvi Banerjee, William L. Romine

Symposium of Student Research, Scholarship, and Creative Activities Materials

Alcohol use disorder is a common health issue in older adults who are facing depression caused by retirement, loss of a spouse, pain, and sleep problems. The prolonged, heavy alcohol ingestion will lead to high alcohol dependency such that cessation or reduction of using alcohol causes alcohol withdrawal syndrome (AWS) in roughly 4 to 72 hours after the last drink. During the initial 8 hours, patients face anxiety, insomnia, nausea, and abdominal pain. This condition is followed by high blood pressure, increased body temperature, unusual heart rate, and confusion. If this syndrome does not receive any treatment, the patients will ...


Defect Characterization Of Additively Manufactured Parts, Sabrina D'Alesandro, Joy Gockel, Andrew Harvey Apr 2020

Defect Characterization Of Additively Manufactured Parts, Sabrina D'Alesandro, Joy Gockel, Andrew Harvey

Symposium of Student Research, Scholarship, and Creative Activities Materials

This document describes various image processing techniques to be used for defect characterization of additively manufactured parts. This will help the reader gain knowledge of materials science engineering and the nuances in analyzing data from image processing software.

Additive manufacturing is shaping the manufacturing world through simplistic household printers’ to more complex metal printers used for a variety of applications. Specifically, laser powder bed fusion (LPBF) is an additive manufacturing process that deposits metal powder over the build plate and melts it with a laser in the shape of the build part. In order to make LPBF more efficient with ...


Identifying Easy Indicators Of Dementia, Swati Padhee, Tanvi Banerjee, Valerie L. Shalin, Krishnaprasad Thirunarayan Apr 2020

Identifying Easy Indicators Of Dementia, Swati Padhee, Tanvi Banerjee, Valerie L. Shalin, Krishnaprasad Thirunarayan

Symposium of Student Research, Scholarship, and Creative Activities Materials

Alzheimer’s Disease (AD) is a degenerative chronic neurodegenerative disease that affects millions of people and whose care costs billions of dollars. There is growing evidence that variations in speech and language may be early indicators of dementia. One of the most initial symptoms of dementia is speech impairment, including difficulty in finding words and changes to the grammatical structure. These early indicators can be detected by having the patients perform a picture description task, such as the Cookie Theft task from the Boston Diagnostic Aphasia Examination. However, much of the state-of-the-art NLP for dementia has been limited due to ...


How Do Surgeon Preferences And Technique Variances Affect Outcome?, Anastasia Axiopoulou, Caroline G. L. Cao, Katherine Lin Md, Keith Watson Md Apr 2020

How Do Surgeon Preferences And Technique Variances Affect Outcome?, Anastasia Axiopoulou, Caroline G. L. Cao, Katherine Lin Md, Keith Watson Md

Symposium of Student Research, Scholarship, and Creative Activities Materials

The goal of the research project is to create a blue-print of a robot-assisted hysterectomy procedure to support design and evaluation of technology to enhance system performance. To create this blue-print, we will conduct a task analysis, model the cognitive task flow and decision making, and develop a simulation of the hysterectomy procedure. The surgical simulation will be used as a platform to train surgeons on robotic-assisted hysterectomies, as well as to assess learning and performance. Additionally, it will be used to design and develop techniques and novel technology to support surgeons in their performance of the surgery. Current research ...


Medical Education And Assisted Surgery By Ar, Sadan Suneesh Menon, Thomas Wischgoll, Sharon Farra, Cindra Holland Jan 2020

Medical Education And Assisted Surgery By Ar, Sadan Suneesh Menon, Thomas Wischgoll, Sharon Farra, Cindra Holland

Computer Science and Engineering Faculty Publications

No abstract provided.


Uncertainty-Aware Brain Lesion Visualization, Christina Gillmann, Dorothee Saur, Thomas Wischgoll, Karl T. Hoffman, Hans Hagen, Ross Maciejewski, Gerik Scheuermann Jan 2020

Uncertainty-Aware Brain Lesion Visualization, Christina Gillmann, Dorothee Saur, Thomas Wischgoll, Karl T. Hoffman, Hans Hagen, Ross Maciejewski, Gerik Scheuermann

Computer Science and Engineering Faculty Publications

A brain lesion is an area of tissue that has been damaged through injury or disease. Its analysis is an essential task for medical researchers to understand diseases and find proper treatments. In this context, visualization approaches became an important tool to locate, quantify, and analyze brain lesions. Unfortunately, image uncertainty highly effects the accuracy of the visualization output. These effects are not covered well in existing approaches, leading to miss-interpretation or a lack of trust in the analysis result. In this work, we present an uncertainty-aware visualization pipeline especially designed forbrain lesions. Our method is based on an uncertainty ...


Eye-Tracking To Evaluate Trust In Human-Atr Interaction, Samuel Francis Adelman Jan 2020

Eye-Tracking To Evaluate Trust In Human-Atr Interaction, Samuel Francis Adelman

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Human collaboration with targeting aids have allowed analysts to achieve a greater level of coordination and productivity in a variety of fields. This project investigates the impact that an Assisted Target Recognition (ATR) algorithm’s false alarm rate and the task Target of Interest (TOI) level has on user-system trust and use in a targeting decision task. Previous studies suggest that an increased number of false alarms in an ATR task negatively impacts analyst trust in the system. This study will further contribute to this research, aiming to provide a better framework for appropriate tolerance levels within ATR algorithms, utilizing ...


Observing P300 Amplitudes In Multiple Sensory Channels Using Cognitive Probing, Cody Lee Wintermute Jan 2020

Observing P300 Amplitudes In Multiple Sensory Channels Using Cognitive Probing, Cody Lee Wintermute

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High cognitive workload occurs when excessive working memory resources have been deployed to resolve sensory and cognitive processing, resulting in decremented task performance. The P300 event-related potential (ERP) component has shown sensitivity to cognitive load, and it was hypothesized that an attenuated P300 amplitude could be indicative of high cognitive load. We tested this hypothesis by having eight participants complete two continual performance tasks at increasing workload levels while simultaneously performing an oddball task, evoking P300 ERPs in either the auditory or tactile sensory channel. In our experiment, electroencephalographic recordings were collected over the parietal region to observe the P300 ...


Extracting Information From Subroutines Using Static Analysis Semantics, Luke A. Burnett Jan 2020

Extracting Information From Subroutines Using Static Analysis Semantics, Luke A. Burnett

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Understanding how a system component can interact with other services can take an immeasurable amount of time. Reverse engineering embedded and large systems can rely on understanding how components interact with one another. This process is time consuming and can sometimes be generalized through certain behavior.We will be explaining two such complicated systems and highlighting similarities between them. We will show that through static analysis you can capture compiler behavior and apply it to the understanding of a function, reducing the total time required to understand a component of whichever system you are learning.


Topological Analysis Of Averaged Sentence Embeddings, Wesley J. Holmes Jan 2020

Topological Analysis Of Averaged Sentence Embeddings, Wesley J. Holmes

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Sentence embeddings are frequently generated by using complex, pretrained models that were trained on a very general corpus of data. This thesis explores a potential alternative method for generating high-quality sentence embeddings for highly specialized corpora in an efficient manner. A framework for visualizing and analyzing sentence embeddings is developed to help assess the quality of sentence embeddings for a highly specialized corpus of documents related to the 2019 coronavirus epidemic. A Topological Data Analysis (TDA) technique is explored as an alternative method for grouping embeddings for document clustering and topic modeling tasks and is compared to a simple clustering ...


Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty Jan 2020

Development Of Real-Time Systems For Supporting Collaborations In Distributed Human And Machine Teams, Aishwarya Bositty

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Real-time distributed systems constitute computing nodes that are connected by a network and coordinate with one another to accomplish a cooperative task, combining the responsiveness, fault-tolerance and geographic independence to support time-constrained collaborative applications, including distributed Human-Machine Teaming. In this thesis research the viability of real-time distributed collaborative technologies is demonstrated through the design, development and validation of prototype systems that support two human-machine teaming scenarios namely, ACE-IMS (Affirmation Cue based Interruption Management Systems) and ReadMI (Real-time Assessment of Dialogue in Motivational Interview). ACE-IMS demonstrates how a combination of AI capabilities and the cloud and mobile computing infrastructure can be ...


Hierarchical Anomaly Detection For Time Series Data, Ryan E. Sperl Jan 2020

Hierarchical Anomaly Detection For Time Series Data, Ryan E. Sperl

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With the rise of Big Data and the Internet of Things, there is an increasing availability of large volumes of real-time streaming data. Unusual occurrences in the underlying system will be reflected in these streams, but any human analysis will quickly become out of date. There is a need for automatic analysis of streaming data capable of identifying these anomalous behaviors as they occur, to give ample time to react. In order to handle many high-velocity data streams, detectors must minimize the processing requirements per value. In this thesis, we have developed a novel anomaly detection method which makes use ...


Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang Jan 2020

Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang

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Sickle cell disease (SCD) is an inherited red blood cell disorder that can cause a multitude of complications throughout a patient's life. Pain is the most common complication and a significant cause of morbidity. Since pain is a highly subjective experience, both medical providers and patients express difficulty in determining ideal treatment and management strategies for pain. Therefore, the development of objective pain assessment and pain forecasting methods is critical to pain management in SCD. On the other hand, the rapidly increasing use of mobile health (mHealth) technology and wearable devices gives the ability to build a remote health ...


Finding Data Races In Software Binaries With Symbolic Execution, Nathan D. Jackson Jan 2020

Finding Data Races In Software Binaries With Symbolic Execution, Nathan D. Jackson

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Modern software applications frequently make use of multithreading to utilize hardware resources better and promote application responsiveness. In these applications, threads share the program state, and synchronization mechanisms ensure proper ordering of accesses to the program state. When a developer fails to implement synchronization mechanisms, data races may occur. Finding data races in an automated way is an already challenging problem, but often impractical without source code or understanding how to execute the program under analysis. In this thesis, we propose a solution for finding data races on software binaries and present our prototype implementation BINRELAY. Our solution makes use ...


Enabling Static Program Analysis Using A Graph Database, Jialun Liu Jan 2020

Enabling Static Program Analysis Using A Graph Database, Jialun Liu

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This thesis presents the design, the implementation, and the evaluation of a database-oriented static program analysis engine for the PHP programming language. This engine analyzes PHP programs by representing their semantics using a graph-based data structure, which will be subsequently stored into a graph database. Such scheme will fundamentally facilitate various program analysis tasks such as static taint analysis, visualization, and data mining. Specifically, these complex program analysis tasks can now be translated into built-in declarative graph database operations with rich features. Our engine fundamentally differs from other existing static program analysis systems that mainly leverage intermediate representation (IRs) to ...


Geoaware - A Simulation-Based Framework For Synthetic Trajectory Generation From Mobility Patterns, Jameson D. Morgan Jan 2020

Geoaware - A Simulation-Based Framework For Synthetic Trajectory Generation From Mobility Patterns, Jameson D. Morgan

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Recent advances in location acquisition services have resulted in vast amounts of trajectory data; providing valuable insight into human mobility. The field of trajectory data mining has exploded as a result, with literature detailing algorithms for (pre)processing, map matching, pattern mining, and the like. Unfortunately, obtaining trajectory data for the design and evaluation of such algorithms is problematic due to privacy, ethical, dataset size, researcher access, and sampling frequency concerns. Synthetic trajectories provide a solution to such a problem as they are cheap to produce and are derived from a fully controllable generation procedure. Citing deficiencies in modern synthetic ...


Design And Simulation Of Boost Dc - Dc Pulse Width Modulator (Pwm) Feed-Forward Control Converter, Calenia L. Franklin Jan 2020

Design And Simulation Of Boost Dc - Dc Pulse Width Modulator (Pwm) Feed-Forward Control Converter, Calenia L. Franklin

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Military aircraft systems’ power losses are occurring during the loading operations; loading and unloading causes the aircraft systems to lose power. The primary aircraft power source is provided by a 400Hz Ground Power Units (GPU). This GPU provides power to interior lighting, the aircraft cargo compartment, and other electrical systems (i.e. bus). The issues are during loading and unloading on the aircraft, which causes dropout of aircraft power supplied by the external 400Hz GPU. The majority of the military aircraft require a high voltage and a high current with a 270V power output. This thesis analyzes using Feed-Forward PWM ...


Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman Jan 2020

Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman

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In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that ...


Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman Jan 2020

Design Of A Novel Wearable Ultrasound Vest For Autonomous Monitoring Of The Heart Using Machine Learning, Garrett G. Goodman

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As the population of older individuals increases worldwide, the number of people with cardiovascular issues and diseases is also increasing. The rate at which individuals in the United States of America and worldwide that succumb to Cardiovascular Disease (CVD) is rising as well. Approximately 2,303 Americans die to some form of CVD per day according to the American Heart Association. Furthermore, the Center for Disease Control and Prevention states that 647,000 Americans die yearly due to some form of CVD, which equates to one person every 37 seconds. Finally, the World Health Organization reports that the number one ...


An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow Jan 2020

An Adversarial Framework For Deep 3d Target Template Generation, Walter E. Waldow

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This paper presents a framework for the generation of 3D models. This is an important problem for many reasons. For example, 3D models are important for systems that are involved in target recognition. These systems use 3D models to train up accuracy on identifying real world object. Traditional means of gathering 3D models have limitations that the generation of 3D models can help overcome. The framework uses a novel generative adversarial network (GAN) that learns latent representations of two dimensional views of a model to bootstrap the network’s ability to learn to generate three dimensional objects. The novel architecture ...


Comparing Rf Fingerprinting Performance Of Hobbyist And Commercial-Grade Sdrs, Travis R. Smith Jan 2020

Comparing Rf Fingerprinting Performance Of Hobbyist And Commercial-Grade Sdrs, Travis R. Smith

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Radio Frequency Fingerprinting (RFF) research typically uses expensive, laboratory grade receivers which have high dynamic range, very stable oscillators, large instantaneous bandwidth, multi-rate sampling, etc. In this study, the RFF effectiveness of lower grade receivers is considered. Using software-defined radios (SDRs) of different cost and performance, a linear regression model is developed to predict RFF performance. Unlike two previous studies of SDR effectiveness that used commercial and lab-grade SDRs, the experiment here focused on hobbyist and commercial-grade SDRs (RTL-SDR, B200-mini, N210). A regression model is proposed for a generic SDR. Using a full-factorial experiment matrix, the gain, sample rate, and ...


Predicting Subjective Sleep Quality Using Objective Measurements In Older Adults, Reza Sadeghi Jan 2020

Predicting Subjective Sleep Quality Using Objective Measurements In Older Adults, Reza Sadeghi

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Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support ...


Quadrotor Uav Flight Control With Integrated Mapping And Path Planning Capabilities, Jason A. Gauthier Jan 2020

Quadrotor Uav Flight Control With Integrated Mapping And Path Planning Capabilities, Jason A. Gauthier

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Quadrotor UAVs have become a common and easily acquirable hardware platform for research and development with control laws, mapping systems, and path planning. In this research, a non-linear model of a quadrotor UAV is linearized with model parameters being identified using collected flight data. The PID, LQR, and backstepping control laws are implemented. An adaptive control law is also implemented to handle the loss of effectiveness in motor actuation. Additionally, this research also implements a laser-based SLAM algorithm for mapping and localization in an unknown two-dimensional environment. Path planning and obstacle avoidance algorithms are implemented onboard using the Robot Operating ...


Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi Jan 2020

Quantitative Susceptibility Mapping (Qsm) Reconstruction From Mri Phase Data, Sara Gharabaghi

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Quantitative susceptibility mapping (QSM) is a powerful technique that reveals changes in the underlying tissue susceptibility distribution. It can be used to measure the concentrations of iron and calcium in the brain both of which are linked with numerous neurodegenerative diseases. However, reconstructing the QSM image from the MRI phase data is an ill-posed inverse problem. Different methods have been proposed to overcome this difficulty. Still, the reconstructed QSM images suffer from streaking artifacts and underestimate the measured susceptibility of deep gray matter, veins, and other high susceptibility regions. This thesis proposes a structurally constrained Susceptibility Weighted Imaging and Mapping ...


Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt Jan 2020

Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt

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Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a ...


Analytical Approach To Multi-Objective Joint Inference Control For Fixed Wing Unmanned Aerial Vehicles, Julian L. Casey Jan 2020

Analytical Approach To Multi-Objective Joint Inference Control For Fixed Wing Unmanned Aerial Vehicles, Julian L. Casey

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Fixed-wing Unmanned Aerial Vehicles (UAVs) have been found highly useful in various environments, including military and law enforcement. With the increased use of fixed-wing UAVs, there becomes an increased need to optimize the resources available. One approach to resource management is to create multi-objective flights. This thesis presents the design, analysis, and experimental implementation of multi-objective resource management for the resource of Range, distance available to the UAV, from the viewpoint of Intelligence Surveillance and Reconnaissance (ISR). First, a Simulation Environment is created capable of tracking multiple fixed-wing UAVs and to allow for the UAVs’ being controlled by an externally ...