ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 77
Description
The development of advanced, anthropomorphic artificial hands aims to provide upper extremity amputees with improved functionality for activities of daily living. However, many state-of-the-art hands have a large number of degrees of freedom that can be challenging to control in an intuitive manner. Automated grip responses could be built into artificial hands in order to enhance grasp stability and reduce the cognitive burden on the user. To this end, three studies were conducted to understand how human hands respond, passively and actively, to unexpected perturbations of a grasped object along and about different axes relative to the hand. The first study investigated the effect of magnitude, direction, and axis of rotation on precision grip responses to unexpected rotational perturbations of a grasped object. A robust "catch-up response" (a rapid, pulse-like increase in grip force rate previously reported only for translational perturbations) was observed whose strength scaled with the axis of rotation. Using two haptic robots, we then investigated the effects of grip surface friction, axis, and direction of perturbation on precision grip responses for unexpected translational and rotational perturbations for three different hand-centric axes. A robust catch-up response was observed for all axes and directions for both translational and rotational perturbations. Grip surface friction had no effect on the stereotypical catch-up response. Finally, we characterized the passive properties of the precision grip-object system via robot-imposed impulse perturbations. The hand-centric axis associated with the greatest translational stiffness was different than that for rotational stiffness. This work expands our understanding of the passive and active features of precision grip, a hallmark of human dexterous manipulation. Biological insights such as these could be used to enhance the functionality of artificial hands and the quality of life for upper extremity amputees.
ContributorsDe Gregorio, Michael (Author) / Santos, Veronica J. (Thesis advisor) / Artemiadis, Panagiotis K. (Committee member) / Santello, Marco (Committee member) / Sugar, Thomas (Committee member) / Helms Tillery, Stephen I. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We test whether motor learning transfer is more related to use of shared neural structures between imagery and motor execution or to more generalized cognitive factors. Using an EEG-BCI, we train one group of participants to control the movements of a cursor using embodied motor imagery. A second group is trained to control the cursor using abstract motor imagery. A third control group practices moving the cursor using an arm and finger on a touch screen. We hypothesized that if motor learning transfer is related to the use of shared neural structures then the embodied motor imagery group would show more learning transfer than the abstract imaging group. If, on the other hand, motor learning transfer results from more general cognitive processes, then the abstract motor imagery group should also demonstrate motor learning transfer to the manual performance of the same task. Our findings support that motor learning transfer is due to the use of shared neural structures between imaging and motor execution of a task. The abstract group showed no motor learning transfer despite being better at EEG-BCI control than the embodied group. The fact that more participants were able to learn EEG-BCI control using abstract imagery suggests that abstract imagery may be more suitable for EEG-BCIs for some disabilities, while embodied imagery may be more suitable for others. In Part 2, EEG data collected in the above experiment was used to train an artificial neural network (ANN) to map EEG signals to machine commands. We found that our open-source ANN using spectrograms generated from SFFTs is fundamentally different and in some ways superior to Emotiv's proprietary method. Our use of novel combinations of existing technologies along with abstract and embodied imagery facilitates adaptive customization of EEG-BCI control to meet needs of individual users.
Contributorsda Silva, Flavio J. K (Author) / Mcbeath, Michael K (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Presson, Clark (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this research, our goal was to fabricate Josephson junctions that can be stably processed at 300°C or higher. With the purpose of integrating Josephson junction fabrication with the current semiconductor circuit fabrication process, back-end process temperatures (>350 °C) will be a key for producing large scale junction circuits reliably, which requires the junctions to be more thermally stable than current Nb/Al-AlOx/Nb junctions. Based on thermodynamics, Hf was chosen to produce thermally stable Nb/Hf-HfOx/Nb superconductor tunnel Josephson junctions that can be grown or processed at elevated temperatures. Also elevated synthesis temperatures improve the structural and electrical properties of Nb electrode layers that could potentially improve junction device performance. The refractory nature of Hf, HfO2 and Nb allow for the formation of flat, abrupt and thermally-stable interfaces. But the current Al-based barrier will have problems when using with high-temperature grown and high-quality Nb. So our work is aimed at using Nb grown at elevated temperatures to fabricate thermally stable Josephson tunnel junctions. As a junction barrier metal, Hf was studied and compared with the traditional Al-barrier material. We have proved that Hf-HfOx is a good barrier candidate for high-temperature synthesized Josephson junction. Hf deposited at 500 °C on Nb forms flat and chemically abrupt interfaces. Nb/Hf-HfOx/Nb Josephson junctions were synthesized, fabricated and characterized with different oxidizing conditions. The results of materials characterization and junction electrical measurements are reported and analyzed. We have improved the annealing stability of Nb junctions and also used high-quality Nb grown at 500 °C as the bottom electrode successfully. Adding a buffer layer or multiple oxidation steps improves the annealing stability of Josephson junctions. We also have attempted to use the Atomic Layer Deposition (ALD) method for the growth of Hf oxide as the junction barrier and got tunneling results.
ContributorsHuang, Mengchu, 1987- (Author) / Newman, Nathan (Thesis advisor) / Rowell, John M. (Committee member) / Singh, Rakesh K. (Committee member) / Chamberlin, Ralph (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
Myoelectric control is lled with potential to signicantly change human-robot interaction.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
Humans desire compliant robots to safely interact in dynamic environments
associated with daily activities. As surface electromyography non-invasively measures
limb motion intent and correlates with joint stiness during co-contractions,
it has been identied as a candidate for naturally controlling such robots. However,
state-of-the-art myoelectric interfaces have struggled to achieve both enhanced
functionality and long-term reliability. As demands in myoelectric interfaces trend
toward simultaneous and proportional control of compliant robots, robust processing
of multi-muscle coordinations, or synergies, plays a larger role in the success of the
control scheme. This dissertation presents a framework enhancing the utility of myoelectric
interfaces by exploiting motor skill learning and
exible muscle synergies for
reliable long-term simultaneous and proportional control of multifunctional compliant
robots. The interface is learned as a new motor skill specic to the controller,
providing long-term performance enhancements without requiring any retraining or
recalibration of the system. Moreover, the framework oers control of both motion
and stiness simultaneously for intuitive and compliant human-robot interaction. The
framework is validated through a series of experiments characterizing motor learning
properties and demonstrating control capabilities not seen previously in the literature.
The results validate the approach as a viable option to remove the trade-o
between functionality and reliability that have hindered state-of-the-art myoelectric
interfaces. Thus, this research contributes to the expansion and enhancement of myoelectric
controlled applications beyond commonly perceived anthropomorphic and
\intuitive control" constraints and into more advanced robotic systems designed for
everyday tasks.
ContributorsIson, Mark (Author) / Artemiadis, Panagiotis (Thesis advisor) / Santello, Marco (Committee member) / Greger, Bradley (Committee member) / Berman, Spring (Committee member) / Sugar, Thomas (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
The ability to shift the photovoltaic (PV) power curve and make the energy accessible during peak hours can be accomplished through pairing solar PV with energy storage technologies. A prototype hybrid air conditioning system (HACS), built under supervision of project head Patrick Phelan, consists of PV modules running a DC compressor that operates a conventional HVAC system paired with a second evaporator submerged within a thermal storage tank. The thermal storage is a 0.284m3 or 75 gallon freezer filled with Cryogel balls, submerged in a weak glycol solution. It is paired with its own separate air handler, circulating the glycol solution. The refrigerant flow is controlled by solenoid valves that are electrically connected to a high and low temperature thermostat. During daylight hours, the PV modules run the DC compressor. The refrigerant flow is directed to the conventional HVAC air handler when cooling is needed. Once the desired room temperature is met, refrigerant flow is diverted to the thermal storage, storing excess PV power. During peak energy demand hours, the system uses only small amounts of grid power to pump the glycol solution through the air handler (note the compressor is off), allowing for money and energy savings. The conventional HVAC unit can be scaled down, since during times of large cooling demands the glycol air handler can be operated in parallel with the conventional HVAC unit. Four major test scenarios were drawn up in order to fully comprehend the performance characteristics of the HACS. Upon initial running of the system, ice was produced and the thermal storage was charged. A simple test run consisting of discharging the thermal storage, initially ~¼ frozen, was performed. The glycol air handler ran for 6 hours and the initial cooling power was 4.5 kW. This initial test was significant, since greater than 3.5 kW of cooling power was produced for 3 hours, thus demonstrating the concept of energy storage and recovery.
ContributorsPeyton-Levine, Tobin (Author) / Phelan, Patrick (Thesis advisor) / Trimble, Steve (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2012
Description
The study of high energy particle irradiation effect on Josephson junction tri-layers is relevant to applications in space and radioactive environments. It also allows us to investigate the influence of defects and interfacial intermixing on the junction electrical characteristics. In this work, we studied the influence of 2MeV Helium ion irradiation with doses up to 5.2×1016 ions/cm2 on the tunneling behavior of Nb/Al/AlOx/Nb Josephson junctions. Structural and analytical TEM characterization, combined with SRIM modeling, indicates that over 4nm of intermixing occurred at the interfaces. EDX analysis after irradiation, suggests that the Al and O compositions from the barrier are collectively distributed together over a few nanometers. Surprisingly, the IV characteristics were largely unchanged. The normal resistance, Rn, increased slightly (<20%) after the initial dose of 3.5×1015 ions/cm2 and remained constant after that. This suggests that tunnel barrier electrical properties were not affected much, despite the significant changes in the chemical distribution of the barrier's Al and O shown in SRIM modeling and TEM pictures. The onset of quasi-particle current, sum of energy gaps (2Δ), dropped systematically from 2.8meV to 2.6meV with increasing dosage. Similarly, the temperature onset of the Josephson current dropped from 9.2K to 9.0K. This suggests that the order parameter at the barrier interface has decreased as a result of a reduced mean free path in the Al proximity layer and a reduction in the transition temperature of the Nb electrode near the barrier. The dependence of Josephson current on the magnetic field and temperature does not change significantly with irradiation, suggesting that intermixing into the Nb electrode is significantly less than the penetration depth.
ContributorsZhang, Tiantian (Author) / Newman, Nathan (Thesis advisor) / Rowell, John M (Committee member) / Singh, Rakesh K. (Committee member) / Chamberlin, Ralph (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
Nanoparticle suspensions, popularly termed “nanofluids,” have been extensively investigated for their thermal and radiative properties. Such work has generated great controversy, although it is arguably accepted today that the presence of nanoparticles rarely leads to useful enhancements in either thermal conductivity or convective heat transfer. On the other hand, there are still examples of unanticipated enhancements to some properties, such as the reported specific heat of molten salt-based nanofluids and the critical heat flux. Another largely overlooked example is the apparent effect of nanoparticles on the effective latent heat of vaporization (hfg) of aqueous nanofluids. A previous study focused on molecular dynamics (MD) modeling supplemented with limited experimental data to suggest that hfg increases with increasing nanoparticle concentration.
Here, this research extends that exploratory work in an effort to determine if hfg of aqueous nanofluids can be manipulated, i.e., increased or decreased, by the addition of graphite or silver nanoparticles. Our results to date indicate that hfg can be substantially impacted, by up to ± 30% depending on the type of nanoparticle. Moreover, this dissertation reports further experiments with changing surface area based on volume fraction (0.005% to 2%) and various nanoparticle sizes to investigate the mechanisms for hfg modification in aqueous graphite and silver nanofluids. This research also investigates thermophysical properties, i.e., density and surface tension in aqueous nanofluids to support the experimental results of hfg based on the Clausius - Clapeyron equation. This theoretical investigation agrees well with the experimental results. Furthermore, this research investigates the hfg change of aqueous nanofluids with nanoscale studies in terms of melting of silver nanoparticles and hydrophobic interactions of graphite nanofluid. As a result, the entropy change due to those mechanisms could be a main cause of the changes of hfg in silver and graphite nanofluids.
Finally, applying the latent heat results of graphite and silver nanofluids to an actual solar thermal system to identify enhanced performance with a Rankine cycle is suggested to show that the tunable latent heat of vaporization in nanofluilds could be beneficial for real-world solar thermal applications with improved efficiency.
Here, this research extends that exploratory work in an effort to determine if hfg of aqueous nanofluids can be manipulated, i.e., increased or decreased, by the addition of graphite or silver nanoparticles. Our results to date indicate that hfg can be substantially impacted, by up to ± 30% depending on the type of nanoparticle. Moreover, this dissertation reports further experiments with changing surface area based on volume fraction (0.005% to 2%) and various nanoparticle sizes to investigate the mechanisms for hfg modification in aqueous graphite and silver nanofluids. This research also investigates thermophysical properties, i.e., density and surface tension in aqueous nanofluids to support the experimental results of hfg based on the Clausius - Clapeyron equation. This theoretical investigation agrees well with the experimental results. Furthermore, this research investigates the hfg change of aqueous nanofluids with nanoscale studies in terms of melting of silver nanoparticles and hydrophobic interactions of graphite nanofluid. As a result, the entropy change due to those mechanisms could be a main cause of the changes of hfg in silver and graphite nanofluids.
Finally, applying the latent heat results of graphite and silver nanofluids to an actual solar thermal system to identify enhanced performance with a Rankine cycle is suggested to show that the tunable latent heat of vaporization in nanofluilds could be beneficial for real-world solar thermal applications with improved efficiency.
ContributorsLee, Soochan (Author) / Phelan, Patrick E (Thesis advisor) / Wu, Carole-Jean (Thesis advisor) / Wang, Robert (Committee member) / Wang, Liping (Committee member) / Taylor, Robert A. (Committee member) / Prasher, Ravi (Committee member) / Arizona State University (Publisher)
Created2015
Description
In recent years, 40% of the total world energy consumption and greenhouse gas emissions is because of buildings. Out of that 60% of building energy consumption is due to HVAC systems. Under current trends these values will increase in coming years. So, it is important to identify passive cooling or heating technologies to meet this need. The concept of thermal energy storage (TES), as noted by many authors, is a promising way to rectify indoor temperature fluctuations. Due to its high energy density and the use of latent energy, Phase Change Materials (PCMs) are an efficient choice to use as TES. A question that has not satisfactorily been addressed, however, is the optimum location of PCM. In other words, given a constant PCM mass, where is the best location for it in a building? This thesis addresses this question by positioning PCM to obtain maximum energy savings and peak time delay. This study is divided into three parts. The first part is to understand the thermal behavior of building surfaces, using EnergyPlus software. For analysis, a commercial prototype building model for a small office in Phoenix, provided by the U.S. Department of Energy, is applied and the weather location file for Phoenix, Arizona is also used. The second part is to justify the best location, which is obtained from EnergyPlus, using a transient grey box building model. For that we have developed a Resistance-Capacitance (RC) thermal network and studied the thermal profile of a building in Phoenix. The final part is to find the best location for PCMs in buildings using EnergyPlus software. In this part, the mass of PCM used in each location remains unchanged. This part also includes the impact of the PCM mass on the optimized location and how the peak shift varies. From the analysis, it is observed that the ceiling is the best location to install PCM for yielding the maximum reduction in HVAC energy consumption for a hot, arid climate like Phoenix.
ContributorsPrem Anand Jayaprabha, Jyothis Anand (Author) / Phelan, Patrick (Thesis advisor) / Wang, Robert (Committee member) / Parrish, Kristen (Committee member) / Arizona State University (Publisher)
Created2018
Description
A novel Monte Carlo rejection technique for solving the phonon and electron
Boltzmann Transport Equation (BTE), including full many-particle interactions, is
presented in this work. This technique has been developed to explicitly model
population-dependent scattering within the full-band Cellular Monte Carlo (CMC)
framework to simulate electro-thermal transport in semiconductors, while ensuring
the conservation of energy and momentum for each scattering event. The scattering
algorithm directly solves the many-body problem accounting for the instantaneous
distribution of the phonons. The general approach presented is capable of simulating
any non-equilibrium phase-space distribution of phonons using the full phonon dispersion
without the need of the approximations commonly used in previous Monte Carlo
simulations. In particular, anharmonic interactions require no assumptions regarding
the dominant modes responsible for anharmonic decay, while Normal and Umklapp
scattering are treated on the same footing.
This work discusses details of the algorithmic implementation of the three particle
scattering for the treatment of the anharmonic interactions between phonons, as well
as treating isotope and impurity scattering within the same framework. The approach
is then extended with a technique based on the multivariable Hawkes point process
that has been developed to model the emission and the absorption process of phonons
by electrons.
The simulation code was validated by comparison with both analytical, numerical,
and experimental results; in particular, simulation results show close agreement with
a wide range of experimental data such as the thermal conductivity as function of the
isotopic composition, the temperature and the thin-film thickness.
Boltzmann Transport Equation (BTE), including full many-particle interactions, is
presented in this work. This technique has been developed to explicitly model
population-dependent scattering within the full-band Cellular Monte Carlo (CMC)
framework to simulate electro-thermal transport in semiconductors, while ensuring
the conservation of energy and momentum for each scattering event. The scattering
algorithm directly solves the many-body problem accounting for the instantaneous
distribution of the phonons. The general approach presented is capable of simulating
any non-equilibrium phase-space distribution of phonons using the full phonon dispersion
without the need of the approximations commonly used in previous Monte Carlo
simulations. In particular, anharmonic interactions require no assumptions regarding
the dominant modes responsible for anharmonic decay, while Normal and Umklapp
scattering are treated on the same footing.
This work discusses details of the algorithmic implementation of the three particle
scattering for the treatment of the anharmonic interactions between phonons, as well
as treating isotope and impurity scattering within the same framework. The approach
is then extended with a technique based on the multivariable Hawkes point process
that has been developed to model the emission and the absorption process of phonons
by electrons.
The simulation code was validated by comparison with both analytical, numerical,
and experimental results; in particular, simulation results show close agreement with
a wide range of experimental data such as the thermal conductivity as function of the
isotopic composition, the temperature and the thin-film thickness.
ContributorsSabatti, Flavio Francesco Maria (Author) / Saraniti, Marco (Thesis advisor) / Smith, David J. (Committee member) / Wang, Robert (Committee member) / Goodnick, Stephen M (Committee member) / Arizona State University (Publisher)
Created2018