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.
Displaying 1 - 10 of 177
Description
Emission of CO2 into the atmosphere has become an increasingly concerning issue as we progress into the 21st century Flue gas from coal-burning power plants accounts for 40% of all carbon dioxide emissions. The key to successful separation and sequestration is to separate CO2 directly from flue gas (10-15% CO2, 70% N2), which can range from a few hundred to as high as 1000°C. Conventional microporous membranes (carbons/silicas/zeolites) are capable of separating CO2 from N2 at low temperatures, but cannot achieve separation above 200°C. To overcome the limitations of microporous membranes, a novel ceramic-carbonate dual-phase membrane for high temperature CO2 separation was proposed. The membrane was synthesized from porous La0.6Sr0.4Co0.8Fe0.2O3-d (LSCF) supports and infiltrated with molten carbonate (Li2CO3/Na2CO3/K2CO3). The CO2 permeation mechanism involves a reaction between CO2 (gas phase) and O= (solid phase) to form CO3=, which is then transported through the molten carbonate (liquid phase) to achieve separation. The effects of membrane thickness, temperature and CO2 partial pressure were studied. Decreasing thickness from 3.0 to 0.375 mm led to higher fluxes at 900°C, ranging from 0.186 to 0.322 mL.min-1.cm-2 respectively. CO2 flux increased with temperature from 700 to 900°C. Activation energy for permeation was similar to that for oxygen ion conduction in LSCF. For partial pressures above 0.05 atm, the membrane exhibited a nearly constant flux. From these observations, it was determined that oxygen ion conductivity limits CO2 permeation and that the equilibrium oxygen vacancy concentration in LSCF is dependent on the partial pressure of CO2 in the gas phase. Finally, the dual-phase membrane was used as a membrane reactor. Separation at high temperatures can produce warm, highly concentrated streams of CO2 that could be used as a chemical feedstock for the synthesis of syngas (H2 + CO). Towards this, three different membrane reactor configurations were examined: 1) blank system, 2) LSCF catalyst and 3) 10% Ni/y-alumina catalyst. Performance increased in the order of blank system < LSCF catalyst < Ni/y-alumina catalyst. Favorable conditions for syngas production were high temperature (850°C), low sweep gas flow rate (10 mL.min-1) and high methane concentration (50%) using the Ni/y-alumina catalyst.
ContributorsAnderson, Matthew Brandon (Author) / Lin, Jerry (Thesis advisor) / Alford, Terry (Committee member) / Rege, Kaushal (Committee member) / Anderson, James (Committee member) / Rivera, Daniel (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis proposes hardware and software security enhancements to the robotic explorer of a capstone team, in collaboration with the NASA Psyche Mission Student Collaborations program. The NASA Psyche Mission, launching in 2022 and reaching the metallic asteroid of the same name in 2026, will explore from orbit what is hypothesized to be remnant core material of an early planet, potentially providing key insights to planet formation. Following this initial mission, it is possible there would be scientists and engineers interested in proposing a mission to land an explorer on the surface of Psyche to further document various properties of the asteroid. As a proposal for a second mission, an interdisciplinary engineering and science capstone team at Arizona State University designed and constructed a robotic explorer for the hypothesized surfaces of Psyche, capable of semi-autonomously navigating simulated surfaces to collect scientific data from onboard sensors. A critical component of this explorer is the command and data handling subsystem, and as such, the security of this system, though outside the scope of the capstone project, remains a crucial consideration. This thesis proposes the pairing of Trusted Platform Module (TPM) technology for increased hardware security and the implementation of SELinux (Security Enhanced Linux) for increased software security for Earth-based testing as well as space-ready missions.
ContributorsAnderson, Kelly Joanne (Author) / Bowman, Catherine (Thesis director) / Kozicki, Michael (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Thin films of ever reducing thickness are used in a plethora of applications and their performance is highly dependent on their microstructure. Computer simulations could then play a vital role in predicting the microstructure of thin films as a function of processing conditions. FACET is one such software tool designed by our research group to model polycrystalline thin film growth, including texture evolution and grain growth of polycrystalline films in 2D. Several modifications to the original FACET code were done to enhance its usability and accuracy. Simulations of sputtered silver thin films are presented here with FACET 2.0 with qualitative and semi-quantitative comparisons with previously published experimental results. Comparisons of grain size, texture and film thickness between simulations and experiments are presented which describe growth modes due to various deposition factors like flux angle and substrate temperature. These simulations provide reasonable agreement with the experimental data over a diverse range of process parameters. Preliminary experiments in depositions of Silver films are also attempted with varying substrates and thickness in order to generate complementary experimental and simulation studies of microstructure evolution. Overall, based on the comparisons, FACET provides interesting insights into thin film growth processes, and the effects of various deposition conditions on thin film structure and microstructure. Lastly, simple molecular dynamics simulations of deposition on bi-crystals are attempted for gaining insight into texture based grain competition during film growth. These simulations predict texture based grain coarsening mechanisms like twinning and grain boundary migration that have been commonly reported in FCC films.
ContributorsRairkar, Asit (Author) / Adams, James B (Thesis advisor) / Krause, Stephen (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2011
Description
Micro-electro-mechanical systems (MEMS) film bulk acoustic resonator (FBAR) demonstrates label-free biosensing capabilities and is considered to be a promising alternative of quartz crystal microbalance (QCM). FBARs achieve great success in vacuum, or in the air, but find limited applications in liquid media because squeeze damping significantly degrades quality factor (Q) and results in poor frequency resolution. A transmission-line model shows that by confining the liquid in a thickness comparable to the acoustic wavelength of the resonator, Q can be considerably improved. The devices exhibit damped oscillatory patterns of Q as the liquid thickness varies. Q assumes its maxima and minima when the channel thickness is an odd and even multiple of the quarter-wavelength of the resonance, respectively. Microfluidic channels are integrated with longitudinal-mode FBARs (L-FBARs) to realize this design; a tenfold improvement of Q over fully-immersed devices is experimentally verified. Microfluidic integrated FBAR sensors have been demonstrated for detecting protein binding in liquid and monitoring the Vroman effect (the competitive protein adsorption behavior), showing their potential as a promising bio-analytical tool. A contour-mode FBAR (C-FBAR) is developed to further improve Q and to alleviate the need for complex integration of microfluidic channels. The C-FBAR consists of a suspended piezoelectric ring made of aluminum nitride and is excited in the fundamental radial-extensional mode. By replacing the squeeze damping with shear damping, high Qs (189 in water and 77 in human whole blood) are obtained in semi-infinite depth liquids. The C-FBAR sensors are characterized by aptamer - thrombin binding pairs and aqueous glycerine solutions for mass and viscosity sensing schemes, respectively. The C-FBAR sensor demonstrates accurate viscosity measurement from 1 to 10 centipoise, and can be deployed to monitor in-vitro blood coagulation processes in real time. Results show that its resonant frequency decreases as the viscosity of the blood increases during the fibrin generation process after the coagulation cascade. The coagulation time and the start/end of the fibrin generation are quantitatively determined, showing the C-FBAR can be a low-cost, portable yet reliable tool for hemostasis diagnostics.
ContributorsXu, Wencheng (Author) / Chae, Junseok (Thesis advisor) / Phillips, Stephen (Committee member) / Cao, Yu (Committee member) / Kozicki, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
The design and development of analog/mixed-signal (AMS) integrated circuits (ICs) is becoming increasingly expensive, complex, and lengthy. Rapid prototyping and emulation of analog ICs will be significant in the design and testing of complex analog systems. A new approach, Programmable ANalog Device Array (PANDA) that maps any AMS design problem to a transistor-level programmable hardware, is proposed. This approach enables fast system level validation and a reduction in post-Silicon bugs, minimizing design risk and cost. The unique features of the approach include 1) transistor-level programmability that emulates each transistor behavior in an analog design, achieving very fine granularity of reconfiguration; 2) programmable switches that are treated as a design component during analog transistor emulating, and optimized with the reconfiguration matrix; 3) compensation of AC performance degradation through boosting the bias current. Based on these principles, a digitally controlled PANDA platform is designed at 45nm node that can map AMS modules across 22nm to 90nm technology nodes. A systematic emulation approach to map any analog transistor to 45nm PANDA cell is proposed, which achieves transistor level matching accuracy of less than 5% for ID and less than 10% for Rout and Gm. Circuit level analog metrics of a voltage-controlled oscillator (VCO) emulated by PANDA, match to those of the original designs in 22nm and 90nm nodes with less than a 5% error. Several other 90nm and 22nm analog blocks are successfully emulated by the 45nm PANDA platform, including a folded-cascode operational amplifier and a sample-and-hold module (S/H). Further capabilities of PANDA are demonstrated by the first full-chip silicon of PANDA which is implemented on 65nm process This system consists of a 24×25 cell array, reconfigurable interconnect and configuration memory. The voltage and current reference circuits, op amps and a VCO with a phase interpolation circuit are emulated by PANDA.
ContributorsSuh, Jounghyuk (Author) / Bakkaloglu, Bertan (Thesis advisor) / Cao, Yu (Committee member) / Ozev, Sule (Committee member) / Kozicki, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications.
ContributorsRajan, Deepta (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality.
ContributorsVemprala, Sai Hemachandra (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.
ContributorsDey, Anindita (Author) / Sundaram, Hari (Thesis advisor) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Digital sound synthesis allows the creation of a great variety of sounds. Focusing on interesting or ecologically valid sounds for music, simulation, aesthetics, or other purposes limits the otherwise vast digital audio palette. Tools for creating such sounds vary from arbitrary methods of altering recordings to precise simulations of vibrating objects. In this work, methods of sound synthesis by re-sonification are considered. Re-sonification, herein, refers to the general process of analyzing, possibly transforming, and resynthesizing or reusing recorded sounds in meaningful ways, to convey information. Applied to soundscapes, re-sonification is presented as a means of conveying activity within an environment. Applied to the sounds of objects, this work examines modeling the perception of objects as well as their physical properties and the ability to simulate interactive events with such objects. To create soundscapes to re-sonify geographic environments, a method of automated soundscape design is presented. Using recorded sounds that are classified based on acoustic, social, semantic, and geographic information, this method produces stochastically generated soundscapes to re-sonify selected geographic areas. Drawing on prior knowledge, local sounds and those deemed similar comprise a locale's soundscape. In the context of re-sonifying events, this work examines processes for modeling and estimating the excitations of sounding objects. These include plucking, striking, rubbing, and any interaction that imparts energy into a system, affecting the resultant sound. A method of estimating a linear system's input, constrained to a signal-subspace, is presented and applied toward improving the estimation of percussive excitations for re-sonification. To work toward robust recording-based modeling and re-sonification of objects, new implementations of banded waveguide (BWG) models are proposed for object modeling and sound synthesis. Previous implementations of BWGs use arbitrary model parameters and may produce a range of simulations that do not match digital waveguide or modal models of the same design. Subject to linear excitations, some models proposed here behave identically to other equivalently designed physical models. Under nonlinear interactions, such as bowing, many of the proposed implementations exhibit improvements in the attack characteristics of synthesized sounds.
ContributorsFink, Alex M (Author) / Spanias, Andreas S (Thesis advisor) / Cook, Perry R. (Committee member) / Turaga, Pavan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013