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Full-Text Articles in Biomedical Engineering and Bioengineering

Optical Vortex And Poincaré Analysis For Biophysical Dynamics, Anindya Majumdar Jan 2019

Optical Vortex And Poincaré Analysis For Biophysical Dynamics, Anindya Majumdar

Dissertations, Master's Theses and Master's Reports

Coherent light - such as that from a laser - on interaction with biological tissues, undergoes scattering. This scattered light undergoes interference and the resultant field has randomly added phases and amplitudes. This random interference pattern is known as speckles, and has been the subject of multiple applications, including imaging techniques. These speckle fields inherently contain optical vortices, or phase singularities. These are locations where the intensity (or amplitude) of the interference pattern is zero, and the phase is undefined.

In the research presented in this dissertation, dynamic speckle patterns were obtained through computer simulations as well as laboratory setups involving scattering ...


Computational Ultrasound Elastography: A Feasibility Study, Yu Wang Jan 2017

Computational Ultrasound Elastography: A Feasibility Study, Yu Wang

Dissertations, Master's Theses and Master's Reports

Ultrasound Elastography (UE) is an emerging set of imaging modalities used to assess the biomechanical properties of soft tissues. UE has been applied to numerous clinical applications. Particularly, results from clinical trials of UE in breast lesion differentiation and staging liver fibrosis indicated that there was a lack of confidence in UE measurements or image interpretation. Confidence on UE measurements interpretation is critically important for improving the clinical utility of UE. The primary objective of my thesis is to develop a computational simulation platform based on open-source software packages including Field II, VTK, FEBio and Tetgen. The proposed virtual simulation ...


Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar Feb 2015

Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar

Derrick K Rollins, Sr.

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn of the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early ...


Objective Comparison Of Toolmarks From The Cutting Surfaces Of Slip-Joint Pliers, Taylor Grieve, L. Scott Chumbley, Jim Kreiser, Max Morris, Laura Ekstrand, Song Zhang Apr 2014

Objective Comparison Of Toolmarks From The Cutting Surfaces Of Slip-Joint Pliers, Taylor Grieve, L. Scott Chumbley, Jim Kreiser, Max Morris, Laura Ekstrand, Song Zhang

Ames Laboratory Publications

Experimental results from a statistical analysis algorithm for objectively comparing toolmarks via data files obtained using optical profilometry data are described. The algorithm employed has successfully been used to compare striated marks produced by screwdrivers. In this study, quasi-striated marks produced by the cutting surfaces of slip-joint pliers were examined. Marks were made by cutting both copper and lead wire. Data files were obtained using an optical profilometer that uses focus variation to determine surface roughness. Early efforts using the comparative algorithm yielded inconclusive results when the comparison parameters used were the same as those employed successfully for screw-driver marks ...


Multiple-Input Subject-Specific Modeling Of Plasma Glucose Concentration For Feedforward Control, Kaylee Renee Kotz, Ali Cinar, Yong Mei, Amy Roggendorf, Elizabeth Littlejohn, Laurie Quinn, Derrick K. Rollins Sr. Jan 2014

Multiple-Input Subject-Specific Modeling Of Plasma Glucose Concentration For Feedforward Control, Kaylee Renee Kotz, Ali Cinar, Yong Mei, Amy Roggendorf, Elizabeth Littlejohn, Laurie Quinn, Derrick K. Rollins Sr.

Chemical and Biological Engineering Publications

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an e ffective ...


Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar Jan 2013

Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar

Chemical and Biological Engineering Publications

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn of the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early ...


An Algorithm For Optimally Fitting A Wiener Model, Lucas P. Beverlin, Derrick K. Rollins Sr., Nisarg Vyas, David Andre Jan 2011

An Algorithm For Optimally Fitting A Wiener Model, Lucas P. Beverlin, Derrick K. Rollins Sr., Nisarg Vyas, David Andre

Chemical and Biological Engineering Publications

The purpose of this work is to present a new methodology for fitting Wiener networks to datasets with a large number of variables. Wiener networks have the ability to model a wide range of data types, and their structures can yield parameters with phenomenological meaning. There are several challenges to fitting such a model: model stiffness, the nonlinear nature of a Wiener network, possible overfitting, and the large number of parameters inherent with large input sets. This work describes a methodology to overcome these challenges by using several iterative algorithms under supervised learning and fitting subsets of the parameters at ...


An Extended Data Mining Method For Identifying Differentially Expressed Assay-Specific Signatures In Functional Genomic Studies, Derrick K. Rollins Sr., Ai-Ling Teh Jan 2010

An Extended Data Mining Method For Identifying Differentially Expressed Assay-Specific Signatures In Functional Genomic Studies, Derrick K. Rollins Sr., Ai-Ling Teh

Chemical and Biological Engineering Publications

Background: Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and ...