Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Franz, Nico M
- Creators: Turaga, Pavan
Description
Weevils are among the most diverse and evolutionarily successful animal lineages on Earth. Their success is driven in part by a structure called the rostrum, which gives weevil heads a characteristic "snout-like" appearance. Nut weevils in the genus Curculio use the rostrum to drill holes into developing fruits and nuts, wherein they deposit their eggs. During oviposition this exceedingly slender structure is bent into a straightened configuration - in some species up to 90° - but does not suffer any damage during this process. The performance of the snout is explained in terms of cuticle biomechanics and rostral curvature, as presented in a series of four interconnected studies. First, a micromechanical constitutive model of the cuticle is defined to predict and reconstruct the mechanical behavior of each region in the exoskeleton. Second, the effect of increased endocuticle thickness on the stiffness and fracture strength of the rostrum is assessed using force-controlled tensile testing. In the third chapter, these studies are integrated into finite element models of the snout, demonstrating that the Curculio rostrum is only able to withstand repeated, extreme bending because of
modifications to the composite structure of the cuticle in the rostral apex. Finally, interspecific differences in the differential geometry of the snout are characterized to elucidate the role of biomechanical constraint in the evolution of rostral morphology for both males and females. Together these studies highlight the significance of cuticle biomechanics - heretofore unconsidered by others - as a source of constraint on the evolution of the rostrum and the mechanobiology of the genus Curculio.
modifications to the composite structure of the cuticle in the rostral apex. Finally, interspecific differences in the differential geometry of the snout are characterized to elucidate the role of biomechanical constraint in the evolution of rostral morphology for both males and females. Together these studies highlight the significance of cuticle biomechanics - heretofore unconsidered by others - as a source of constraint on the evolution of the rostrum and the mechanobiology of the genus Curculio.
ContributorsJansen, Michael Andrew (Author) / Franz, Nico M (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Harrison, Jon (Committee member) / Martins, Emilia (Committee member) / Arizona State University (Publisher)
Created2009
Description
Semantic image segmentation has been a key topic in applications involving image processing and computer vision. Owing to the success and continuous research in the field of deep learning, there have been plenty of deep learning-based segmentation architectures that have been designed for various tasks. In this thesis, deep-learning architectures for a specific application in material science; namely the segmentation process for the non-destructive study of the microstructure of Aluminum Alloy AA 7075 have been developed. This process requires the use of various imaging tools and methodologies to obtain the ground-truth information. The image dataset obtained using Transmission X-ray microscopy (TXM) consists of raw 2D image specimens captured from the projections at every beam scan. The segmented 2D ground-truth images are obtained by applying reconstruction and filtering algorithms before using a scientific visualization tool for segmentation. These images represent the corrosive behavior caused by the precipitates and inclusions particles on the Aluminum AA 7075 alloy. The study of the tools that work best for X-ray microscopy-based imaging is still in its early stages.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
ContributorsBarboza, Daniel (Author) / Turaga, Pavan (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020