Matching Items (136)
Description
How can we change what it means to be a human? Products can be used that will allow for near-instantaneous communication with one’s friends and family wherever they are: and the newest devices do not have to be even carried around, as they can be worn instead. Wearable electronics are quickly becoming very popular, with 232.0 million wearable devices sold in 2015. This report provides an overview of current and developing wearable devices, investigates the characteristics of the average buyer for these different types of devices. Finally, marketing strategies are suggested. This work was completed in conjunction with a capstone project with Intel, where three objectives were achieved: First, a universal strain tester that could strain samples cyclically in a manner similar to the body was designed. This equipment was especially designed to be flexible in the testing conditions it could be exposed to, so samples could be tested at elevated temperatures or even underwater. Next, dogbone shaped samples for the testing of Young’s Modulus and elongation to failure were produced, and the cut quality of laser, water-jet, and die-cutting was compared in order to select the most defect-free method for reliable testing. Polydimethylsiloxane (PDMS) is a fantastic candidate material for wearable electronics, however there is some discrepancies in the literature—such as from Eleni et. al—about the impact of ultraviolet radiation on the mechanical properties. By conducting accelerated aging tests simulating up to five years exposure to the sun, it was determined that ultraviolet-induced cross-linking of the polymer chains does occur, leading to severe embrittlement (strain to failure reduced from 3.27 to 0.06 in some cases, reduction to approximately 0.21 on average). As simulated tests of possible usage conditions required strains of at least 0.50-0.70, a variety of solutions were suggested to reduce this embrittlement. This project can lead to standardization of wearables electronics testing methods for more reliable predictions about the device behavior, whether that device is a simple pedometer or something that allows the visually impaired to “see”, such as Toyota’s Blaid.
ContributorsNiebroski, Alexander Wayne (Author) / Adams, James (Thesis director) / Anwar, Shahriar (Committee member) / Materials Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The work for this thesis was done in conjunction to that of my capstone project, which focused on understanding the effects of powder re-use on products built via Direct Metal Laser Sintering (DMLS), a specific additive manufacturing (AM) technique where powder particles are sintered together to form final parts. Honeywell Aerospace helped support this research by providing materials and mentorship; this work will play a key role in their decision to implement DMLS and other AM methods on a larger scale. Whereas the capstone focuses on the technical details of constructing characterization equipment, analyzing data, and formulating a concluding recommendation on whether the powder can be re-used, the thesis attempts to put this body of work in its greater context, surveying the economic and environmental effects of additive manufacturing technologies with a slant towards the aerospace industry. Shifts in the supply chain with aircraft parts and how this affects costs are explored, as well as how the quality and reliability of additively manufactured parts differs from their traditionally manufactured counterparts and the effects of this on related industries and purchasers.
ContributorsMurella, Anoosha Sainagaki (Author) / Adams, James (Thesis director) / Tasooji, Amaneh (Committee member) / Materials Science and Engineering Program (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The goal of the paper was to examine the fatigue mechanisms of polymers and silicone based elastomers. The mechanisms of fatigue due to crazing: the alignment of polymer chains to the stress axis, and shear banding: the localized orientation of the polymer by the shear stresses from two planes, were discussed in depth in this paper. Crazing only occurs in tensile stress, is initiated on the surface of the material, and only occurs in brittle polymers. Crazing also accounts for a 40-60% decrease in density, causing localized weakening of the material and a concentration in stress. This is due to a decrease in effective cross sectional area. The mechanism behind discontinuous growth bands was also discussed to be the cause of cyclic crazing. Shear banding only occurs in ductile polymers and can result in the failure of polymers via necking. Furthermore, the high fatigue resistance of silicone elastomers was discussed in this paper. This conclusion was made because of the lack of fatigue mechanisms (crazing, discontinuous growth bands, and shears banding) in the observed elastomer's microstructure after the samples had undergone fatigue tests. This was done through an analysis of room temperature vulcanized silicone adhesives, a heat-curing silicone elastomer, and a self-curing transparent silicone rubber. Fatigue of room temperature vulcanized silicon was observed, however this was reasoned to be the failure of the adhesion of the elastomer to the steel substrate instead of the microstructure itself. Additionally, the significance of fatigue in real world applications was discussed using SouthWest Airlines Flight 812 as an example.
ContributorsWong, Christopher Stanley (Author) / Adams, James (Thesis director) / Krause, Stephen (Committee member) / Anwar, Shahriar (Committee member) / Materials Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Obesity and related health disparities including type 2 diabetes disproportionately impact Latino youth. These health disparities may be the result of gene-environment interactions, but limited research has examined these interactions in the pediatric age group. Lifestyle intervention is the cornerstone for preventing diabetes among high-risk populations and epigenetic and genetic factors may help explain the biological mechanisms underlying diabetes risk reduction following lifestyle changes. MicroRNAs (miRNAs) are small, non-coding RNA’s that regulate gene expression and have emerged as potential biomarkers for predicting type 2 diabetes risk in adults but have yet to be applied to youth. Therefore, the purpose of this study was to identify changes in miRNA expression among Latino youth with prediabetes (4 female/2 male, ages 14-16, BMI percentile 99 ±.2) who participated in a 12-week lifestyle intervention focused on increasing physical activity and improving nutrition-related behaviors.
ContributorsKarch, Jamie (Co-author) / Day, Samantha (Co-author) / Shaibi, Gabriel (Thesis director) / Coletta, Dawn (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Organic light-emitting diodes (OLEDs) have been successfully implemented in various display applications owing to rapid advancements in material design and device architecture. Their success in the display industry has ignited a rising interest in applying OLEDs for solid-state lighting applications through the development of white OLEDs (WOLEDs). However, to enter the market as a serious competitor, WOLEDs must achieve excellent color quality, high external quantum efficiency (EQE) as well as a long operational lifetime. In this research, novel materials and device architectures were explored to improve the performance of single-stack WOLEDs. A new Pt-based phosphorescent emitter, Pt2O2-p2m, was examined as a single emissive emitter for the development of a stable and efficient single-doped WOLED. A bilayer structure was employed to balance the charges carriers within the emissive layer resulting in low efficiency roll-off at high brightness, realizing a peak EQE of 21.5% and EQEs of 20% at 1000 cd m-2 and 15.3% at 7592 cd m-2. A novel phosphorescent/fluorescent, or hybrid, WOLED device architecture was also proposed. To gather a thorough understanding of blue fluorescent OLEDs prior to its use in a WOLED, a study was conducted to investigate the impact of the material selection on the device performance. The use of an anthracene type host demonstrated an improvement to the operational stability of the blue OLED by reducing the occurrence of degradation events. Additionally, various dopant concentrations and blocking materials revealed vastly different efficiency and lifetime results. Finally, a Pd (II) complex, Pd3O8-Py5, with efficient amber-colored aggregate emission was employed to produce a WOLED. Various host materials were investigated to achieve balanced white emission and the addition of an interlayer composed of a high triplet energy material was used to reduce quenching effects. Through this strategy, a color stable WOLED device with a peak EQE of 45% and an estimated LT95 over 50,000 hours at 1000 cd m-2 was realized. The comprehensive performance of the proposed device architecture competes with WOLED devices that are commercially available and reported within the literature domain, providing a strong foundation to further advance the development of highly efficient and stable single-stack WOLEDs.
ContributorsAmeri, Lydia (Author) / Li, Jian (Thesis advisor) / Adams, James (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2022
Description
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022
Description
Neural tissue is a delicate system comprised of neurons and their synapses, glial cells for support, and vasculature for oxygen and nutrient delivery. This complexity ultimately gives rise to the human brain, a system researchers have become increasingly interested in replicating for artificial intelligence purposes. Some have even gone so far as to use neuronal cultures as computing hardware, but utilizing an environment closer to a living brain means having to grapple with the same issues faced by clinicians and researchers trying to treat brain disorders. Most outstanding among these are the problems that arise with invasive interfaces. Optical techniques that use fluorescent dyes and proteins have emerged as a solution for noninvasive imaging with single-cell resolution in vitro and in vivo, but feeding in information in the form of neuromodulation still requires implanted electrodes. The implantation process of these electrodes damages nearby neurons and their connections, causes hemorrhaging, and leads to scarring and gliosis that diminish efficacy. Here, a new approach for noninvasive neuromodulation with high spatial precision is described. It makes use of a combination of ultrasound, high frequency acoustic energy that can be focused to submillimeter regions at significant depths, and electric fields, an effective tool for neuromodulation that lacks spatial precision when used in a noninvasive manner. The hypothesis is that, when combined in a specific manner, these will lead to nonlinear effects at neuronal membranes that cause cells only in the region of overlap to be stimulated. Computational modeling confirmed this combination to be uniquely stimulating, contingent on certain physical effects of ultrasound on cell membranes. Subsequent in vitro experiments led to inconclusive results, however, leaving the door open for future experimentation with modified configurations and approaches. The specific combination explored here is also not the only untested technique that may achieve a similar goal.
ContributorsNester, Elliot (Author) / Wang, Yalin (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2022