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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- All Subjects: Electrical Engineering
- Creators: Turaga, Pavan
- Creators: Honsberg, Christiana
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
Description
As crystalline silicon solar cells continue to get thinner, the recombination of carriers at the surfaces of the cell plays an ever-important role in controlling the cell efficiency. One tool to minimize surface recombination is field effect passivation from the charges present in the thin films applied on the cell surfaces. The focus of this work is to understand the properties of charges present in the SiNx films and then to develop a mechanism to manipulate the polarity of charges to either negative or positive based on the end-application. Specific silicon-nitrogen dangling bonds (·Si-N), known as K center defects, are the primary charge trapping defects present in the SiNx films. A custom built corona charging tool was used to externally inject positive or negative charges in the SiNx film. Detailed Capacitance-Voltage (C-V) measurements taken on corona charged SiNx samples confirmed the presence of a net positive or negative charge density, as high as +/- 8 x 1012 cm-2, present in the SiNx film. High-energy (~ 4.9 eV) UV radiation was used to control and neutralize the charges in the SiNx films. Electron-Spin-Resonance (ESR) technique was used to detect and quantify the density of neutral K0 defects that are paramagnetically active. The density of the neutral K0 defects increased after UV treatment and decreased after high temperature annealing and charging treatments. Etch-back C-V measurements on SiNx films showed that the K centers are spread throughout the bulk of the SiNx film and not just near the SiNx-Si interface. It was also shown that the negative injected charges in the SiNx film were stable and present even after 1 year under indoor room-temperature conditions. Lastly, a stack of SiO2/SiNx dielectric layers applicable to standard commercial solar cells was developed using a low temperature (< 400 °C) PECVD process. Excellent surface passivation on FZ and CZ Si substrates for both n- and p-type samples was achieved by manipulating and controlling the charge in SiNx films.
ContributorsSharma, Vivek (Author) / Bowden, Stuart (Thesis advisor) / Schroder, Dieter (Committee member) / Honsberg, Christiana (Committee member) / Roedel, Ronald (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this thesis, quantitative evaluation of quality of movement during stroke rehabilitation will be discussed. Previous research on stroke rehabilitation in hospital has been shown to be effective. In this thesis, we study various issues that arise when creating a home-based system that can be deployed in a patient's home. Limitation of motion capture due to reduced number of sensors leads to problems with design of kinematic features for quantitative evaluation. Also, the hierarchical three-level tasks of rehabilitation requires new design of kinematic features. In this thesis, the design of kinematic features for a home based stroke rehabilitation system will be presented. Results of the most challenging classifier are shown and proves the effectiveness of the design. Comparison between modern classification techniques and low computational cost threshold based classification with same features will also be shown.
ContributorsCheng, Long (Author) / Turaga, Pavan (Thesis advisor) / Arizona State University (Publisher)
Created2012
Description
Motion capture using cost-effective sensing technology is challenging and the huge success of Microsoft Kinect has been attracting researchers to uncover the potential of using this technology into computer vision applications. In this thesis, an upper-body motion analysis in a home-based system for stroke rehabilitation using novel RGB-D camera - Kinect is presented. We address this problem by first conducting a systematic analysis of the usability of Kinect for motion analysis in stroke rehabilitation. Then a hybrid upper body tracking approach is proposed which combines off-the-shelf skeleton tracking with a novel depth-fused mean shift tracking method. We proposed several kinematic features reliably extracted from the proposed inexpensive and portable motion capture system and classifiers that correlate torso movement to clinical measures of unimpaired and impaired. Experiment results show that the proposed sensing and analysis works reliably on measuring torso movement quality and is promising for end-point tracking. The system is currently being deployed for large-scale evaluations.
ContributorsDu, Tingfang (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Rikakis, Thanassis (Committee member) / Arizona State University (Publisher)
Created2012
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Effective modeling of high dimensional data is crucial in information processing and machine learning. Classical subspace methods have been very effective in such applications. However, over the past few decades, there has been considerable research towards the development of new modeling paradigms that go beyond subspace methods. This dissertation focuses on the study of sparse models and their interplay with modern machine learning techniques such as manifold, ensemble and graph-based methods, along with their applications in image analysis and recovery. By considering graph relations between data samples while learning sparse models, graph-embedded codes can be obtained for use in unsupervised, supervised and semi-supervised problems. Using experiments on standard datasets, it is demonstrated that the codes obtained from the proposed methods outperform several baseline algorithms. In order to facilitate sparse learning with large scale data, the paradigm of ensemble sparse coding is proposed, and different strategies for constructing weak base models are developed. Experiments with image recovery and clustering demonstrate that these ensemble models perform better when compared to conventional sparse coding frameworks. When examples from the data manifold are available, manifold constraints can be incorporated with sparse models and two approaches are proposed to combine sparse coding with manifold projection. The improved performance of the proposed techniques in comparison to sparse coding approaches is demonstrated using several image recovery experiments. In addition to these approaches, it might be required in some applications to combine multiple sparse models with different regularizations. In particular, combining an unconstrained sparse model with non-negative sparse coding is important in image analysis, and it poses several algorithmic and theoretical challenges. A convex and an efficient greedy algorithm for recovering combined representations are proposed. Theoretical guarantees on sparsity thresholds for exact recovery using these algorithms are derived and recovery performance is also demonstrated using simulations on synthetic data. Finally, the problem of non-linear compressive sensing, where the measurement process is carried out in feature space obtained using non-linear transformations, is considered. An optimized non-linear measurement system is proposed, and improvements in recovery performance are demonstrated in comparison to using random measurements as well as optimized linear measurements.
ContributorsNatesan Ramamurthy, Karthikeyan (Author) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Karam, Lina (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
GaAs-based solar cells have attracted much interest because of their high conversion efficiencies of ~28% under one sun illumination. The main carrier recombination mechanisms in the GaAs-based solar cells are surface recombination, radiative recombination and non-radiative recombination. Photon recycling reduces the effect of radiative recombination and is an approach to obtain the device performance described by detailed balance theory. The photon recycling model has been developed and was applied to investigate the loss mechanisms in the state-of-the-art GaAs-based solar cell structures using PC1D software. A standard fabrication process of the GaAs-based solar cells is as follows: wafer preparation, individual cell isolation by mesa, n- and p-type metallization, rapid thermal annealing (RTA), cap layer etching, and anti-reflection coating (ARC). The growth rate for GaAs-based materials is one of critical factors to determine the cost for the growth of GaAs-based solar cells. The cost for fabricating GaAs-based solar cells can be reduced if the growth rate is increased without degrading the crystalline quality. The solar cell wafers grown at different growth rates of 14 μm/hour and 55 μm/hour were discussed in this work. The structural properties of the wafers were characterized by X-ray diffraction (XRD) to identify the crystalline quality, and then the as-grown wafers were fabricated into solar cell devices under the same process conditions. The optical and electrical properties such as surface reflection, external quantum efficiency (EQE), dark I-V, Suns-Voc, and illuminated I-V under one sun using a solar simulator were measured to compare the performances of the solar cells with different growth rates. Some simulations in PC1D have been demonstrated to investigate the reasons of the different device performances between fast growth and slow growth structures. A further analysis of the minority carrier lifetime is needed to investigate into the difference in device performances.
ContributorsZhang, Chaomin (Author) / Honsberg, Christiana (Thesis advisor) / Goodnick, Stephen (Committee member) / Faleev, Nikolai (Committee member) / Arizona State University (Publisher)
Created2014
Description
In the interest of expediting future pilot line start-ups for solar cell research, the development of Arizona State University's student-led pilot line at the Solar Power Laboratory is discussed extensively within this work. Several experiments and characterization techniques used to formulate and optimize a series of processes for fabricating diffused-junction, screen-printed silicon solar cells are expounded upon. An experiment is conducted in which the thickness of a PECVD deposited anti-reflection coating (ARC) is varied across several samples and modeled as a function of deposition time. Using this statistical model in tandem with reflectance measurements for each sample, the ARC thickness is optimized to increase light trapping in the solar cells. A response surface model (RSM) experiment is conducted in which 3 process parameters are varied on the PECVD tool for the deposition of the ARCs on several samples. A contactless photoconductance decay (PCD) tool is used to measure the dark saturation currents of these samples. A statistical analysis is performed using JMP in which optimum deposition parameters are found. A separate experiment shows an increase in the passivation quality of the a-SiNx:H ARCs deposited on the solar cells made on the line using these optimum parameters. A RSM experiment is used to optimize the printing process for a particular silver paste in a similar fashion, the results of which are confirmed by analyzing the series resistance of subsequent cells fabricated on the line. An in-depth explanation of a more advanced analysis using JMP and PCD measurements on the passivation quality of 3 aluminum back-surface fields (BSF) is given. From this experiment, a comparison of the means is conducted in order to choose the most effective BSF paste for cells fabricated on the line. An experiment is conducted in parallel which confirms the results via Voc measurements. It is shown that in a period of 11 months, the pilot line went from producing a top cell efficiency of 11.5% to 17.6%. Many of these methods used for the development of this pilot line are equally applicable to other cell structures, and can easily be applied to other solar cell pilot lines.
ContributorsPickett, Guy (Author) / Bowden, Stuart (Thesis advisor) / Honsberg, Christiana (Committee member) / Bertoni, Mariana (Committee member) / Arizona State University (Publisher)
Created2014
Description
Silicon (Si) solar cells are the dominant technology used in the Photovoltaics industry. Field-effect passivation by means of electrostatic charges stored in an overlying insulator on a silicon solar cell has been proven to be a significantly efficient way to reduce effective surface recombination velocity and increase minority carrier lifetime. Silicon nitride (SiNx) films have been extensively used as passivation layers. The capability to store charges makes SiNx a promising material for excellent feild effect passivation. In this work, symmetrical Si/SiO2/SiNx stacks are developed to study the effect of charges in SiNx films. SiO2 films work as barrier layers. Corona charging technique showed the ability to inject charges into the SiNx films in a short time. Minority carrier lifetimes of the Czochralski (CZ) Si wafers increased significantly after either positive or negative charging. A fast and contactless method to characterize the charged overlying insulators on Si wafer through lifetime measurements is proposed and studied in this work, to overcome the drawbacks of capacitance-voltage (CV) measurements such as time consuming, induction of contanmination and hysteresis effect, etc. Analytical simulations showed behaviors of inverse lifetime (Auger corrected) vs. minority carrier density curves depend on insulator charge densities (Nf). From the curve behavior, the Si surface condition and region of Nf can be estimated. When the silicon surface is at high strong inversion or high accumulation, insulator charge density (Nf) or surface recombination velocity parameters (Sn0 and Sp0) can be determined from the slope of inverse lifetime curves, if the other variable is known. If Sn0 and Sp0 are unknown, Nf values of different samples can be compared as long as all have similar Sn0 and Sp0 values. Using the saturation current density (J0) and intercept fit extracted from the lifetime measurement, the bulk lifetime can be calculated. Therefore, this method is feasible and promising for charged insulator characterization.
ContributorsYang, Qun (Author) / Bowden, Stuart (Thesis advisor) / Honsberg, Christiana (Committee member) / Tracy, Clarence (Committee member) / Arizona State University (Publisher)
Created2014
Description
Continuous monitoring of sensor data from smart phones to identify human activities and gestures, puts a heavy load on the smart phone's power consumption. In this research study, the non-Euclidean geometry of the rich sensor data obtained from the user's smart phone is utilized to perform compressive analysis and efficient classification of human activities by employing machine learning techniques. We are interested in the generalization of classical tools for signal approximation to newer spaces, such as rotation data, which is best studied in a non-Euclidean setting, and its application to activity analysis. Attributing to the non-linear nature of the rotation data space, which involve a heavy overload on the smart phone's processor and memory as opposed to feature extraction on the Euclidean space, indexing and compaction of the acquired sensor data is performed prior to feature extraction, to reduce CPU overhead and thereby increase the lifetime of the battery with a little loss in recognition accuracy of the activities. The sensor data represented as unit quaternions, is a more intrinsic representation of the orientation of smart phone compared to Euler angles (which suffers from Gimbal lock problem) or the computationally intensive rotation matrices. Classification algorithms are employed to classify these manifold sequences in the non-Euclidean space. By performing customized indexing (using K-means algorithm) of the evolved manifold sequences before feature extraction, considerable energy savings is achieved in terms of smart phone's battery life.
ContributorsSivakumar, Aswin (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014