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 93
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The processing power and storage capacity of portable devices have improved considerably over the past decade. This has motivated the implementation of sophisticated audio and other signal processing algorithms on such mobile devices. Of particular interest in this thesis is audio/speech processing based on perceptual criteria. Specifically, estimation of parameters from human auditory models, such as auditory patterns and loudness, involves computationally intensive operations which can strain device resources. Hence, strategies for implementing computationally efficient human auditory models for loudness estimation have been studied in this thesis. Existing algorithms for reducing computations in auditory pattern and loudness estimation have been examined and improved algorithms have been proposed to overcome limitations of these methods. In addition, real-time applications such as perceptual loudness estimation and loudness equalization using auditory models have also been implemented. A software implementation of loudness estimation on iOS devices is also reported in this thesis. In addition to the loudness estimation algorithms and software, in this thesis project we also created new illustrations of speech and audio processing concepts for research and education. As a result, a new suite of speech/audio DSP functions was developed and integrated as part of the award-winning educational iOS App 'iJDSP." These functions are described in detail in this thesis. Several enhancements in the architecture of the application have also been introduced for providing the supporting framework for speech/audio processing. Frame-by-frame processing and visualization functionalities have been developed to facilitate speech/audio processing. In addition, facilities for easy sound recording, processing and audio rendering have also been developed to provide students, practitioners and researchers with an enriched DSP simulation tool. Simulations and assessments have been also developed for use in classes and training of practitioners and students.
ContributorsKalyanasundaram, Girish (Author) / Spanias, Andreas S (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
ContributorsHe, Miao (Author) / Zhang, Junshan (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Hedman, Kory (Committee member) / Si, Jennie (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Despite significant advances in digital pathology and automation sciences, current diagnostic practice for cancer detection primarily relies on a qualitative manual inspection of tissue architecture and cell and nuclear morphology in stained biopsies using low-magnification, two-dimensional (2D) brightfield microscopy. The efficacy of this process is limited by inter-operator variations in sample preparation and imaging, and by inter-observer variability in assessment. Over the past few decades, the predictive value quantitative morphology measurements derived from computerized analysis of micrographs has been compromised by the inability of 2D microscopy to capture information in the third dimension, and by the anisotropic spatial resolution inherent to conventional microscopy techniques that generate volumetric images by stacking 2D optical sections to approximate 3D. To gain insight into the analytical 3D nature of cells, this dissertation explores the application of a new technology for single-cell optical computed tomography (optical cell CT) that is a promising 3D tomographic imaging technique which uses visible light absorption to image stained cells individually with sub-micron, isotropic spatial resolution. This dissertation provides a scalable analytical framework to perform fully-automated 3D morphological analysis from transmission-mode optical cell CT images of hematoxylin-stained cells. The developed framework performs rapid and accurate quantification of 3D cell and nuclear morphology, facilitates assessment of morphological heterogeneity, and generates shape- and texture-based biosignatures predictive of the cell state. Custom 3D image segmentation methods were developed to precisely delineate volumes of interest (VOIs) from reconstructed cell images. Comparison with user-defined ground truth assessments yielded an average agreement (DICE coefficient) of 94% for the cell and its nucleus. Seventy nine biologically relevant morphological descriptors (features) were computed from the segmented VOIs, and statistical classification methods were implemented to determine the subset of features that best predicted cell health. The efficacy of our proposed framework was demonstrated on an in vitro model of multistep carcinogenesis in human Barrett's esophagus (BE) and classifier performance using our 3D morphometric analysis was compared against computerized analysis of 2D image slices that reflected conventional cytological observation. Our results enable sensitive and specific nuclear grade classification for early cancer diagnosis and underline the value of the approach as an objective adjunctive tool to better understand morphological changes associated with malignant transformation.
ContributorsNandakumar, Vivek (Author) / Meldrum, Deirdre R (Thesis advisor) / Nelson, Alan C. (Committee member) / Karam, Lina J (Committee member) / Ye, Jieping (Committee member) / Johnson, Roger H (Committee member) / Bussey, Kimberly J (Committee member) / Arizona State University (Publisher)
Created2013
Description
In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
ContributorsWang, Xufei (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The recent spotlight on concussion has illuminated deficits in the current standard of care with regard to addressing acute and persistent cognitive signs and symptoms of mild brain injury. This stems, in part, from the diffuse nature of the injury, which tends not to produce focal cognitive or behavioral deficits that are easily identified or tracked. Indeed it has been shown that patients with enduring symptoms have difficulty describing their problems; therefore, there is an urgent need for a sensitive measure of brain activity that corresponds with higher order cognitive processing. The development of a neurophysiological metric that maps to clinical resolution would inform decisions about diagnosis and prognosis, including the need for clinical intervention to address cognitive deficits. The literature suggests the need for assessment of concussion under cognitively demanding tasks. Here, a joint behavioral- high-density electroencephalography (EEG) paradigm was employed. This allows for the examination of cortical activity patterns during speech comprehension at various levels of degradation in a sentence verification task, imposing the need for higher-order cognitive processes. Eight participants with concussion listened to true-false sentences produced with either moderately to highly intelligible noise-vocoders. Behavioral data were simultaneously collected. The analysis of cortical activation patterns included 1) the examination of event-related potentials, including latency and source localization, and 2) measures of frequency spectra and associated power. Individual performance patterns were assessed during acute injury and a return visit several months following injury. Results demonstrate a combination of task-related electrophysiology measures correspond to changes in task performance during the course of recovery. Further, a discriminant function analysis suggests EEG measures are more sensitive than behavioral measures in distinguishing between individuals with concussion and healthy controls at both injury and recovery, suggesting the robustness of neurophysiological measures during a cognitively demanding task to both injury and persisting pathophysiology.
ContributorsUtianski, Rene (Author) / Liss, Julie M (Thesis advisor) / Berisha, Visar (Committee member) / Caviness, John N (Committee member) / Dorman, Michael (Committee member) / Arizona State University (Publisher)
Created2014
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
Everyday speech communication typically takes place face-to-face. Accordingly, the task of perceiving speech is a multisensory phenomenon involving both auditory and visual information. The current investigation examines how visual information influences recognition of dysarthric speech. It also explores where the influence of visual information is dependent upon age. Forty adults participated in the study that measured intelligibility (percent words correct) of dysarthric speech in auditory versus audiovisual conditions. Participants were then separated into two groups: older adults (age range 47 to 68) and young adults (age range 19 to 36) to examine the influence of age. Findings revealed that all participants, regardless of age, improved their ability to recognize dysarthric speech when visual speech was added to the auditory signal. The magnitude of this benefit, however, was greater for older adults when compared with younger adults. These results inform our understanding of how visual speech information influences understanding of dysarthric speech.
ContributorsFall, Elizabeth (Author) / Liss, Julie (Thesis advisor) / Berisha, Visar (Committee member) / Gray, Shelley (Committee member) / Arizona State University (Publisher)
Created2014