Matching Items (106)
Description
In this thesis I introduce a new direction to computing using nonlinear chaotic dynamics. The main idea is rich dynamics of a chaotic system enables us to (1) build better computers that have a flexible instruction set, and (2) carry out computation that conventional computers are not good at it. Here I start from the theory, explaining how one can build a computing logic block using a chaotic system, and then I introduce a new theoretical analysis for chaos computing. Specifically, I demonstrate how unstable periodic orbits and a model based on them explains and predicts how and how well a chaotic system can do computation. Furthermore, since unstable periodic orbits and their stability measures in terms of eigenvalues are extractable from experimental times series, I develop a time series technique for modeling and predicting chaos computing from a given time series of a chaotic system. After building a theoretical framework for chaos computing I proceed to architecture of these chaos-computing blocks to build a sophisticated computing system out of them. I describe how one can arrange and organize these chaos-based blocks to build a computer. I propose a brand new computer architecture using chaos computing, which shifts the limits of conventional computers by introducing flexible instruction set. Our new chaos based computer has a flexible instruction set, meaning that the user can load its desired instruction set to the computer to reconfigure the computer to be an implementation for the desired instruction set. Apart from direct application of chaos theory in generic computation, the application of chaos theory to speech processing is explained and a novel application for chaos theory in speech coding and synthesizing is introduced. More specifically it is demonstrated how a chaotic system can model the natural turbulent flow of the air in the human speech production system and how chaotic orbits can be used to excite a vocal tract model. Also as another approach to build computing system based on nonlinear system, the idea of Logical Stochastic Resonance is studied and adapted to an autoregulatory gene network in the bacteriophage λ.
ContributorsKia, Behnam (Author) / Ditto, William (Thesis advisor) / Huang, Liang (Committee member) / Lai, Ying-Cheng (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT Group III-nitride semiconductor materials have been commercially used in fabrication of light-emitting diodes (LEDs) and laser diodes (LDs) covering the spectral range from UV to visible and infrared, and exhibit unique properties suitable for modern optoelectronic applications. Great advances have recently happened in the research and development in high-power and high-efficiency blue-green-white LEDs, blue LDs and other optoelectronic applications. However, there are still many unsolved challenges with these materials. In this dissertation, several issues concerning structural, electronic and optical properties of III-nitrides have been investigated using a combination of transmission electron microscopy (TEM), electron holography (EH) and cathodoluminescence (CL) techniques. First, a trend of indium chemical inhomogeneity has been found as the indium composition increases for the InGaN epitaxial layers grown by hydride vapor phase epitaxy. Second, different mechanisms contributing to the strain relaxation have been studied for non-polar InGaN epitaxial layers grown on zinc oxide (ZnO) substrate. Third, various structural morphologies of non-polar InGaN epitaxial layers grown on free-standing GaN substrate have been investigated. Fourth, the effect of the growth temperature on the performance of GaN lattice-matched InAlN electron blocking layers has been studied. Finally, the electronic and optical properties of GaN nanowires containing a AlN/GaN superlattice structure have been investigated showing relatively small internal electric field and superlattice- and defect-related emissions along the nanowires.
ContributorsSun, Kewei (Author) / Ponce, Fernando (Thesis advisor) / Smith, David (Committee member) / Treacy, Michael (Committee member) / Drucker, Jeffery (Committee member) / Schmidt, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this dissertation, remote plasma interactions with the surfaces of low-k interlayer dielectric (ILD), Cu and Cu adhesion layers are investigated. The first part of the study focuses on the simultaneous plasma treatment of ILD and chemical mechanical polishing (CMP) Cu surfaces using N2/H2 plasma processes. H atoms and radicals in the plasma react with the carbon groups leading to carbon removal for the ILD films. Results indicate that an N2 plasma forms an amide-like layer on the surface which apparently leads to reduced carbon abstraction from an H2 plasma process. In addition, FTIR spectra indicate the formation of hydroxyl (Si-OH) groups following the plasma exposure. Increased temperature (380 °C) processing leads to a reduction of the hydroxyl group formation compared to ambient temperature processes, resulting in reduced changes of the dielectric constant. For CMP Cu surfaces, the carbonate contamination was removed by an H2 plasma process at elevated temperature while the C-C and C-H contamination was removed by an N2 plasma process at elevated temperature. The second part of this study examined oxide stability and cleaning of Ru surfaces as well as consequent Cu film thermal stability with the Ru layers. The ~2 monolayer native Ru oxide was reduced after H-plasma processing. The thermal stability or islanding of the Cu film on the Ru substrate was characterized by in-situ XPS. After plasma cleaning of the Ru adhesion layer, the deposited Cu exhibited full coverage. In contrast, for Cu deposition on the Ru native oxide substrate, Cu islanding was detected and was described in terms of grain boundary grooving and surface and interface energies. The thermal stability of 7 nm Ti, Pt and Ru ii interfacial adhesion layers between a Cu film (10 nm) and a Ta barrier layer (4 nm) have been investigated in the third part. The barrier properties and interfacial stability have been evaluated by Rutherford backscattering spectrometry (RBS). Atomic force microscopy (AFM) was used to measure the surfaces before and after annealing, and all the surfaces are relatively smooth excluding islanding or de-wetting phenomena as a cause of the instability. The RBS showed no discernible diffusion across the adhesion layer/Ta and Ta/Si interfaces which provides a stable underlying layer. For a Ti interfacial layer RBS indicates that during 400 °C annealing Ti interdiffuses through the Cu film and accumulates at the surface. For the Pt/Cu system Pt interdiffuion is detected which is less evident than Ti. Among the three adhesion layer candidates, Ru shows negligible diffusion into the Cu film indicating thermal stability at 400 °C.
ContributorsLiu, Xin (Author) / Nemanich, Robert (Thesis advisor) / Chamberlin, Ralph (Committee member) / Chen, Tingyong (Committee member) / Smith, David (Committee member) / Ponce, Fernando (Committee member) / Arizona State University (Publisher)
Created2012
Description
The energy band gap of a semiconductor material critically influences the operating wavelength of an optoelectronic device. Realization of any desired band gap, or even spatially graded band gaps, is important for applications such as lasers, light-emitting diodes (LEDs), solar cells, and detectors. Compared to thin films, nanowires offer greater flexibility for achieving a variety of alloy compositions. Furthermore, the nanowire geometry permits simultaneous incorporation of a wide range of compositions on a single substrate. Such controllable alloy composition variation can be realized either within an individual nanowire or between distinct nanowires across a substrate. This dissertation explores the control of spatial composition variation in ternary alloy nanowires. Nanowires were grown by the vapor-liquid-solid (VLS) mechanism using chemical vapor deposition (CVD). The gas-phase supersaturation was considered in order to optimize the deposition morphology. Composition and structure were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy (EDS), and x-ray diffraction (XRD). Optical properties were investigated through photoluminescence (PL) measurements. The chalcogenides selected as alloy endpoints were lead sulfide (PbS), cadmium sulfide (CdS), and cadmium selenide (CdSe). Three growth modes of PbS were identified, which included contributions from spontaneously generated catalyst. The resulting wires were found capable of lasing with wavelengths over 4000 nm, representing the longest known wavelength from a sub-wavelength wire. For CdxPb1-xS nanowires, it was established that the cooling process significantly affects the alloy composition and structure. Quenching was critical to retain metastable alloys with x up to 0.14, representing a new composition in nanowire form. Alternatively, gradual cooling caused phase segregation, which created heterostructures with light emission in both the visible and mid-infrared regimes. The CdSSe alloy system was fully explored for spatial composition variation. CdSxSe1-x nanowires were grown with composition variation across the substrate. Subsequent contact printing preserved the designed composition gradient and led to the demonstration of a variable wavelength photodetector device. CdSSe axial heterostructure nanowires were also achieved. The growth process involved many variables, including a deliberate and controllable change in substrate temperature. As a result, both red and green light emission was detected from single nanowires.
ContributorsNichols, Patricia (Author) / Ning, Cun-Zheng (Thesis advisor) / Carpenter, Ray (Committee member) / Bennett, Peter (Committee member) / Smith, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Of the potential technologies for pre-combustion capture, membranes offer the advantages of being temperature resistant, able to handle large flow rates, and having a relatively small footprint. A significant amount of research has centered on the use of polymeric and microporous inorganic membranes to separate CO2. These membranes, however, have limitations at high temperature resulting in poor permeation performance. To address these limitations, the use of a dense dual-phase membrane has been studied. These membranes are composed of conductive solid and conductive liquid phases that have the ability to selectively permeate CO2 by forming carbonate ions that diffuse through the membrane at high temperature. The driving force for transport through the membrane is a CO2 partial pressure gradient. The membrane provides a theoretically infinite selectivity. To address stability of the ceramic-carbonate dual-phase membrane for CO2 capture at high temperature, the ceramic phase of the membrane was studied and replaced with materials previously shown to be stable in harsh conditions. The permeation properties and stability of La0.6Sr0.4Co0.8Fe0.2O3-δ (LSCF)-carbonate, La0.85Ce0.1Ga0.3Fe0.65Al0.05O3-δ (LCGFA)-carbonate, and Ce0.8Sm0.2O1.9 (SDC)-carbonate membranes were examined under a wide range of experimental conditions at high temperature. LSCF-carbonate membranes were shown to be unstable without the presence of O2 due to reaction of CO2 with the ceramic phase. In the presence of O2, however, the membranes showed stable permeation behavior for more than one month at 900oC. LCGFA-carbonate membranes showed great chemical and permeation stability in the presence of various conditions including exposure to CH4 and H2, however, the permeation performance was quite low when compared to membranes in the literature. Finally, SDC-carbonate membranes showed great chemical and permeation stability both in a CO2:N2 environment for more than two weeks at 900oC as well as more than one month of exposure to simulated syngas conditions at 700oC. Ceramic phase chemical stability increased in the order of LSCF < LCGFA < SDC while permeation performance increased in the order of LCGFA < LSCF < SDC.
ContributorsNorton, Tyler (Author) / Lin, Jerry Y.S. (Thesis advisor) / Alford, Terry (Committee member) / Lind, Mary Laura (Committee member) / Smith, David (Committee member) / Torres, Cesar (Committee member) / Arizona State University (Publisher)
Created2013
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012