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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Description
Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Federal education policies call for school district leaders to promote classroom technology integration to prepare students with 21st century skills. However, schools are struggling to integrate technology effectively, with students often reporting that they feel like they need to power down and step back in time technologically when they enter classrooms. The lack of meaningful technology use in classrooms indicates a need for increased teacher preparation. The purpose of this study was to investigate the impact a coaching model of professional development had on school administrators` abilities to increase middle school teachers` technology integration in their classrooms. This study attempted to coach administrators to develop and articulate a vision, cultivate a culture, and model instruction relative to the meaningful use of instructional technology. The study occurred in a middle school. Data for this case study were collected via administrator interviews, the Principal`s Computer Technology Survey, structured observations using the Higher Order Thinking, Engaged Learning, Authentic Learning, Technology Use protocol, field notes, the Technology Integration Matrix, teacher interviews, and a research log. Findings concluded that cultivating change in an organization is a complex process that requires commitment over an extended period of time. The meaningful use of instructional technology remained minimal at the school during fall 2010. My actions as a change agent informed the school`s administrators about the role meaningful use of technology can play in instruction. Limited professional development, administrative vision, and expectations minimized the teachers` meaningful use of instructional technology; competing priorities and limited time minimized the administrators` efforts to improve the meaningful use of instructional technology. Realizing that technology proficient teachers contribute to student success with technology, it may be wise for administrators to incorporate technology-enriched professional development and exercise their leadership abilities to promote meaningful technology use in classrooms.
ContributorsRobertson, Kristen (Author) / Moore, David (Thesis advisor) / Cheatham, Greg (Committee member) / Catalano, Ruth (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study utilized symbolic interaction as a framework to examine the impact of mobility on four veteran elementary general music teachers' identities, roles, and perceptions of role support. Previous research has focused on teacher identity formation among preservice and novice teachers; veteran teachers are less frequently represented in the literature. Teacher mobility research has focused on student achievement, teachers' reasons for moving, and teacher attrition. The impact of mobility on veteran teachers' identities, roles, and perceptions of role support has yet to be considered. A multiple case design was employed for this study. The criteria for purposeful selection of the participants were elementary general music teachers who had taught for at least ten years, who had changed teaching contracts and taught in at least two different schools, and who were viewed as effective music educators by fine arts coordinators. Data were collected over a period of eight months through semi-structured interviews, email correspondence, observations, review of videotapes of the participants' teaching in previous schools, and collection of artifacts. Data were analyzed within and across cases. The cross-case analysis revealed themes within the categories of identity, role, and role support for the participants. The findings suggest that the participants perceived their music teacher roles as multi-dimensional. They claimed their core identities remained stable over time; however, shifts in teacher identity occurred throughout their years as teachers. The participants asserted that mobility at the start of their careers had a positive impact because they each were challenged to solidify their own teacher identities and music teacher roles in varied school contexts. Mobility negatively impacted role and teacher practices during times when the participants adjusted to new school climates and role expectations. Role support varied depending upon school context, and the participants discovered active involvement in the school community was an effective means of seeking and acquiring role support. Reflection experiences in music teacher preparation programs, as well as mentoring and professional development geared toward teacher identity formation and role maturation, may assist teachers in matching their desired school context with their teacher identities and perceptions of the music teacher role.
ContributorsGray, Lori F (Author) / Stauffer, Sandra (Committee member) / Schmidt, Margaret (Committee member) / Sullivan, Jill (Committee member) / Bush, Jeffrey (Committee member) / Tobias, Evan (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purposes of this study were (a) to develop a reliable and valid measure of secondary student attitudes toward band teacher turnover using the Thurstone (1928) equal-appearing interval scale as a model, and (b) to administer this measurement tool to determine attitudes of high school band students toward teacher turnover. This procedure included collecting statements about an imagined teacher turnover from students in the population (N = 216) and having student judges (N = 95) sort the statements into eleven categories based on how positive, neutral, or negative, each statement was perceived. The judging results were then analyzed, and 29 statements were selected for inclusion in the final survey, which was completed by students (N = 521) from 10 randomly selected high schools in Arizona. Student responses were analyzed and compared by the independent variables of gender, grade level, and band teacher turnover experience, to determine if significant differences existed. Results indicated that the overall students' attitudes toward teacher turnover are neutral. One significant difference was found in the slightly positive attitudes of students in the year immediately following a band teacher turnover. This only lasts a year, as students in the second year of a teacher turnover were found to have comparable attitudes to students who have not experienced a new teacher transition. Findings also suggest seniors may have a different perspective than other students toward teacher turnover.
ContributorsKloss, Thomas E (Author) / Sullivan, Jill (Thesis advisor) / Stauffer, Sandra (Committee member) / Schmidt, Margaret (Committee member) / Bush, Jeffrey (Committee member) / Tobias, Evan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Dr. Jerold D. Ottley's twenty-five years leading the Mormon Tabernacle Choir resulted in many distinguished awards and recognitions for the ensemble. Included among these are two Platinum and three Gold records from the Recording Industry Association of America, an Emmy from the Academy of Television Arts and Sciences, and two Freedom Foundation Awards for service to the country. He conducted the Choir at two presidential inaugurations, Ronald Reagan's in 1981 and George H. W. Bush's in 1989, as well as performances at the 1984 Los Angeles Olympics Gala. He presided over eleven international tours to twenty-six countries and crisscrossed the United States for engagements in nearly every region of the country. Despite the awards, commendations, and increased recognition of the Choir, Ottley's greatest contributions were largely internal to the organization. Jerold Ottley is a skilled music educator, administrator, and emissary. Application of these proficiencies while at the helm of the Choir, led to what are, arguably, his three largest contributions: 1) as educator, he instituted in-service training for choir members, raising the level of their individual musicianship, thereby improving the technical level of the entire Choir; 2) as administrator, Ottley created policies and procedures that resulted in a more disciplined, refined ensemble; and 3) as emissary, he raised the ensemble's reputation among the general public and with music professionals. For the general public, he significantly broadened the Choir's repertoire and traveled frequently thereby reaching a wider audience. He secured greater respect among music professionals by inviting many of them to work directly with the Choir. The results were unparalleled. Ottley's twenty-five year tenure with the Choir is reflected in broader audiences, increased professional acceptance, added organizational discipline, and unprecedented musical proficiency. It is a notable legacy for a man who reportedly never felt comfortable as director of the Mormon Tabernacle Choir.
ContributorsArchibald, Lyle Jay (Author) / Gentry, Gregory (Thesis advisor) / Britton, David (Committee member) / DeMars, James (Committee member) / Doan, Jerry (Committee member) / Solis, Theodore (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011