Biomedical Engineering and Bioengineering Commons^™

Numerical Investigation Of Subglottal Stenosis Effects On Human Voice Production, Dariush Bodaghi

Electronic Theses and Dissertations

This dissertation aimed to advance knowledge of how subglottal stenosis impacts voice production physiology. An in-house fluid-structure-acoustic interaction approach based on the hydrodynamic/acoustic splitting technique was employed. This technique was rigorously verified for simulating phonation by matching the acoustic behavior to a compressible flow solver for phonation-relevant geometries. Simulations of an idealized 2D vocal tract model demonstrated the effects of supraglottal acoustic resonance on vocal fold kinematics and glottal flow waveform. Results showed that the acoustic coupling between higher harmonics and formats generated pressure oscillations, modifying vocal fold dynamics and glottal flow rate.

A major novelty was the incorporation and …

Digital Twins Of The Living Knee: From Measurements To Model, Thor Erik Andreassen

Electronic Theses and Dissertations

Modern medicine has dramatically improved the lives of many. In orthopaedics, robotic surgery has given clinicians superior accuracy when performing interventions over conventional methods. Nevertheless, while these and many other methods are available to ensure treatments are performed successfully, far fewer methods exist to predict the proper treatment option for a given person. Clinicians are forced to categorize individuals, choosing the best treatment on “average.” However, many individuals differ significantly from the “average” person, for which many of these treatments are designed. Going forward, a method of testing, evaluating, and predicting different treatment options' short- and long-term effects on an …

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 …

Advancing Medical Technology For Motor Impairment Rehabilitation: Tools, Protocols, And Devices, Matthew Yough

Graduate Theses, Dissertations, and Problem Reports

Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson’s Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due …

Application Of Deep Learning For Medical Sciences And Epidemiology Data Analysis And Diagnostic Modeling, Somenath Chakraborty

Dissertations

Machine Learning and Artificial Intelligence have made significant progress concurrent with new advancements in hardware and software technologies. Deep learning methods heavily utilize parallel computing and Graphical Processing Units(GPU). It is already used in many applications ranging from image classification, object detection, segmentation, cyber security problems and others. Deep Learning is emerging as a viable choice in dealing with today’s real-time medical problems. We need new methods and technologies in the field of Medical Science and Epidemiology for detecting and diagnosing emerging threats from new viruses such as COVID-19. The use of Artificial Intelligence in these domains is becoming more …

Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed

Dissertations, Theses, and Capstone Projects

Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery and many other applications. Chemical fingerprints derived from chemical structures are a widely used feature to build machine learning models. However, the fingerprints ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in …

Numerical And Scaling Study On Application Of Inkjet Technology To Automotive Coating, Masoud Arabghahestani Dr.

Theses and Dissertations--Mechanical Engineering

A thorough literature review identified lack of precision control over quality of droplets generated by the currently available industrial sprayers and a growing need for higher quality droplets in the coating industry. Particularly, lack of knowledge and understanding in continuous inkjets (CIJ) and drop-on-demand (DOD) technologies is identified as significant. Motivated by these needs, this dissertation is dedicated to computational fluid dynamics (CFD) and scaling studies to improve existing inkjet technologies and develop new designs of efficient coating with single and/or multiple piezoelectric sensors to produce on-demand droplets. This dissertation study aims at developing a new DOD type coating technology, …

Machine-Learning-Based Approach To Decoding Physiological And Neural Signals, Elnaz Lashgari

Computational and Data Sciences (PhD) Dissertations

In recent years, machine learning algorithms have been developing rapidly, becoming increasingly powerful tools in decoding physiological and neural signals. The aim of this dissertation is to develop computational tools, and especially machine learning techniques, to identify the most effective methods for feature extraction and classification of these signals. This is particularly challenging due to the highly non-linear, non-stationery, and artifact- and noise-prone nature of these signals.

Among basic human-control tasks, reaching and grasping are ubiquitous in everyday life. I investigated different linear and non-linear dimensionality reduction techniques for feature extraction and classification of electromyography (EMG) during a reach-grasp-lift task. …

Influence Of Extracellular Matrix Stiffness And Topography On Neuronal Cell Behavior And Neurite Outgrowth, Mohan Yasodharababu

Graduate Theses and Dissertations

The focus of regeneration therapy for traumatic brain injuries and Alzheimer's disease is on the promotion and growth of neuronal cells. In vitro research attempts to improve this by modifying the stiffness and topography of the extracellular matrix (ECM). However, the limitations of in vitro experiments make it difficult to control the individual factors influencing neuronal cell growth. A computational model can be used to decouple individual factors and study them individually to gain a better understanding of the mechanics between the neuronal cell and ECM, which will aid in the design of in vitro experimental studies.

This study develops …

Expanding Image Datasets For Deep Learning By Evaluating Independence Through Coefficient Correlation And Mean-Squared Error, Ayman Yousef

Biomedical Engineering Undergraduate Honors Theses

With deep learning being leveraged more regularly in the field of image classification, particularly in medical imaging, network optimizations have become a field in and of itself. With open source, comprehensive medical image datasets few and far, computational dataset expansion has become a useful tool for researchers without the ability to further manually collect data. However, with the rich amount of data that imaging modalities like multi-photon microscopy collect at a time, there is potential to expand datasets through proper utilization of this data that often time goes unused. Previous deep learning studies have shown that improper expansion can conflate …

Multiphysics Computational Model Of Fluid Flow And Mass Transport In Aneurysm, Tanja Cupac

Electronic Theses and Dissertations

The abdominal aortic aneurysm is progressive, asymptomatic, and can eventually lead to rupture which is a catastrophic event leading to massive internal bleeding and possibly death. AAA cases have been characterized by the development of an intraluminal thrombus (ILT). The ILT correlates with the progression of hypoxia in the arterial wall. The extent that ILT presence reduces oxygen flux to the wall has not been quantified and there is rather a poor understanding of key parameters that can affect thrombus-mediated oxygen transport in AAA. The purpose of this study is to address this gap and to assess the effects of …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Development Of A Statistical Shape-Function Model Of The Implanted Knee For Real-Time Prediction Of Joint Mechanics, Kalin Gibbons

Boise State University Theses and Dissertations

Outcomes of total knee arthroplasty (TKA) are dependent on surgical technique, patient variability, and implant design. Non-optimal design or alignment choices may result in undesirable contact mechanics and joint kinematics, including poor joint alignment, instability, and reduced range of motion. Implant design and surgical alignment are modifiable factors with potential to improve patient outcomes, and there is a need for robust implant designs that can accommodate patient variability. Our objective was to develop a statistical shape-function model (SFM) of a posterior stabilized implant knee to instantaneously predict output mechanics in an efficient manner. Finite element methods were combined with Latin …

Mechanochemical Regulation Of Epithelial Tissue Remodeling: A Multiscale Computational Model Of The Epithelial-Mesenchymal Transition Program, Lewis Scott

Theses and Dissertations

Epithelial-mesenchymal transition (EMT) regulates the cellular processes of migration, growth, and proliferation - as well as the collective cellular process of tissue remodeling - in response to mechanical and chemical stimuli in the cellular microenvironment. Cells of the epithelium form cell-cell junctions with adjacent cells to function as a barrier between the body and its environment. By distributing localized stress throughout the tissue, this mechanical coupling between cells maintains tensional homeostasis in epithelial tissue structures and provides positional information for regulating cellular processes. Whereas in vitro and in vivo models fail to capture the complex interconnectedness of EMT-associated signaling networks, …

A Study Of Scalability And Cost-Effectiveness Of Large-Scale Scientific Applications Over Heterogeneous Computing Environment, Arghya K. Das

LSU Doctoral Dissertations

Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data.

In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures …

Supporting Engineering Design Of Additively Manufactured Medical Devices With Knowledge Management Through Ontologies, Thomas Hagedorn

Doctoral Dissertations

Medical environments pose a substantial challenge for engineering designers. They combine significant knowledge demands with large investment for new product development and severe consequences in the case of design failure. Engineering designers must contend with an often-chaotic environment to which they have limited access and familiarity, a user base that is difficult to engage and highly diverse in many attributes, and a market structure that often pits stakeholders against one another. As medical care in general moves towards personalized models and surgical tools towards less invasive options emerging manufacturing technologies in additive manufacturing offer significant potential for the design of …

An Application Of M-Matrices To Preserve Bounded Positive Solutions To The Evolution Equations Of Biofilm Models, Richard S. Landry Jr.

University of New Orleans Theses and Dissertations

In this work, we design a linear, two step implicit finite difference method to approximate the solutions of a biological system that describes the interaction between a microbial colony and a surrounding substrate. Three separate models are analyzed, all of which can be described as systems of partial differential equations (PDE)s with nonlinear diffusion and reaction, where the biological colony grows and decays based on the substrate bioavailability. The systems under investigation are all complex models describing the dynamics of biological films. In view of the difficulties to calculate analytical solutions of the models, we design here a numerical technique …

Poly Drop, Zachary T. Scott, Lilly J. Paul

Computer Engineering

Poly Drop is a software interface to control an Open Drop digital micro-fluidics system. We obtained a hardware system from Gaudi labs. Our task was to create a Graphical User Interface that made the control of the device easier and more automated for better testing. We created software that had 3 parts: a control GUI, arduino code to control the hardware, and Image Analysis that gives the user information such as location and color of liquid drops as they move across the electrode grid of the Open Drop system. The GUI was developed using Java Swing. The communication between the …

Detection Of Leukocytes Stained With Acridine Orange Using Unique Spectral Features Acquired From An Image-Based Spectrometer, Courtney J. Hunter

Biomedical Engineering Undergraduate Honors Theses

A leukocyte differential count can be used to diagnosis a myriad blood disorders, such as infections, allergies, and efficacy of disease treatments. In recent years, attention has been focused on developing point-of-care (POC) systems to provide this test in global health settings. Acridine orange (AO) is an amphipathic, vital dye that intercalates leukocyte nucleic acids and acidic vesicles. It has been utilized by POC systems to identify the three main leukocyte subtypes: granulocytes, monocytes, and lymphocytes. Subtypes of leukocytes can be characterized using a fluorescence microscope, where the AO has a 450 nm excitation wavelength and has two peak emission …

A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis, Kaushik Bhimraj

Electronic Theses and Dissertations

Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user's neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement …

Action Potential Simulation Of The Hirudo Medicinalis's Retzius Cell In Matlab, Zechari Ryan Tempesta

Master's Theses

Modification of Hodgkin and Huxley’s experimentally derived set of nonlinear differential equations was implemented to accurately simulate the action potential of the Hirudo Medicinalis’s Retzius cell in MATLAB under analogous conditions to those found in the Retzius cell environment. The voltage-gated sodium and potassium channel responses to changes in membrane potential, as experimentally determined by Hodgkin and Huxley, were manipulated to suit simulation parameters established by electrophysiological Retzius cell recordings. Application of this methodology permitted additional accurate simulation of the Hirudo Medicinalis’s P cell under analogous conditions to those found in the P cell environment. Further refinement of this technique …

Prostate Cancer Biomarker Identification: A Comparative Study, Wenjuan Jiang

Computational Modeling & Simulation Engineering Theses & Dissertations

With the current development of proteomics techniques, the discovery of potential molecular biomarkers for early detection of prostate cancer has been greatly improved. In this thesis, we implemented five classifiers including the support vector machine (SVM), Bayesian Classifier, Decision Tree, Random Forest and the Multilayer perceptron (MLP) to test their effectiveness in prostate cancer biomarker identification using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue imaging data. The classifiers were utilized to discriminate cancer mass spectra from normal ones for the data collected at the Eastern Virginia Medical School (EVMS). There are 94 7 spectra from normal tissues and 27 from …

Diseño Y Construcción De Un Dispositivo Electrónico Para La Detección De Obstáculos, Como Ayuda A Personas Con Discapacidad Visual, Juan Elias Rincón Ruiz

Ingeniería en Automatización

Las señales de ultrasonido pueden ser de gran ayuda para la ubicación en el espacio de las personas que sufren de algún tipo de discapacidad visual ya sea total o parcial. Es tanto así, que universidades y empresas dedicadas a la rehabilitación de personas invidentes investigan sobre la posibilidad de hacer que sensores de ultrasonido de alta fidelidad puedan reemplazar el ojo humano, dándole así al paciente, un buen entendimiento sobre las cosas que le rodean, aplicando el principio de ubicación de animales como los murciélagos, delfines u otros que utilicen esta particular característica. La metodología utilizada para la elaboración …