Matching Items (661)
150019-Thumbnail Image.png
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

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
150026-Thumbnail Image.png
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

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
149794-Thumbnail Image.png
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

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
150353-Thumbnail Image.png
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description

Consider Steven Cryos’ words, “When disaster strikes, the time to prepare has passed.” Witnessing domestic water insecurity in events such as Hurricane Katrina, the instability in Flint, Michigan, and most recently the winter storms affecting millions across Texas, we decided to take action. The period between a water supply’s disruption

Consider Steven Cryos’ words, “When disaster strikes, the time to prepare has passed.” Witnessing domestic water insecurity in events such as Hurricane Katrina, the instability in Flint, Michigan, and most recently the winter storms affecting millions across Texas, we decided to take action. The period between a water supply’s disruption and restoration is filled with anxiety, uncertainty, and distress -- particularly since there is no clear indication of when, exactly, restoration comes. It is for this reason that Water Works now exists. As a team of students from diverse backgrounds, what started as an honors project with the Founders Lab at Arizona State University became the seed that will continue to mature into an economically sustainable business model supporting the optimistic visions and tenants of humanitarianism. By having conversations with community members, conducting market research, competing for funding and fostering progress amid the COVID-19 pandemic, our team’s problem-solving traverses the disciplines. The purpose of this paper is to educate our readers about a unique solution to emerging issues of water insecurity that are nested across and within systems who could benefit from the introduction of a personal water reclamation system, showcase our team’s entrepreneurial journey, and propose future directions that will this once pedagogical exercise to continue fulfilling its mission: To heal, to hydrate and to help bring safe water to everyone.

ContributorsReitzel, Gage Alexander (Co-author) / Filipek, Marina (Co-author) / Sadiasa, Aira (Co-author) / Byrne, Jared (Thesis director) / Sebold, Brent (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Human Evolution & Social Change (Contributor, Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
148110-Thumbnail Image.png
Description

This thesis identifies and explains two main problems students face during their internships. The first problem relates to feeling bored at internships due to the simplicity of projects or lack of work. From the interviews conducted, several strategies to avoid this boredom were created, including having employers design education plans

This thesis identifies and explains two main problems students face during their internships. The first problem relates to feeling bored at internships due to the simplicity of projects or lack of work. From the interviews conducted, several strategies to avoid this boredom were created, including having employers design education plans for interns to further their knowledge in programs such as excel during their downtime. The second problem with internships discovered focuses on the gap between what is taught in schools versus what is expected of interns in practice. This thesis identifies several opportunities for improvement in education and strategies on how to handle feeling overwhelmed on intern projects due to lack of knowledge.

ContributorsKomarnyckyj, Katya (Author) / Byrne, Jared (Thesis director) / Crawford, Cassidy (Committee member) / School of Molecular Sciences (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

The market for searching for food online is exploding. According to one expert at Google, “there are over 1 billion restaurant searches on Google every month” (Kelso, 2020). To capture this market and ride the general digital trend of internet personalization (as evidenced by Google search results, ads, YouTube and

The market for searching for food online is exploding. According to one expert at Google, “there are over 1 billion restaurant searches on Google every month” (Kelso, 2020). To capture this market and ride the general digital trend of internet personalization (as evidenced by Google search results, ads, YouTube and social media algorithms, etc), we created Munch to be an algorithm meant to help people find food they’ll love. <br/><br/>Munch offers the ability to search for food by restaurant or even as specific as a menu item (ex: search for the best Pad Thai). The best part? It is customized to your preferences based on a quiz you take when you open the app and from that point continuously learns from your behavior.<br/><br/>This thesis documents the journey of the team who founded Munch, what progress we made and the reasoning behind our decisions, where this idea fits in a competitive marketplace, how much it could be worth, branding, and our recommendations for a successful app in the future.

ContributorsInocencio, Phillippe Adriane (Co-author) / Rajan, Megha (Co-author) / Krug, Hayden (Co-author) / Byrne, Jared (Thesis director) / Sebold, Brent (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

The purpose of this study is to create and establish an efficient and cost-effective solution to decrease the effects of sedentarism in pregnant women. Our team was given a propelling question, from which we had to narrow down our scope and conduct primary and secondary research to determine our ideal

The purpose of this study is to create and establish an efficient and cost-effective solution to decrease the effects of sedentarism in pregnant women. Our team was given a propelling question, from which we had to narrow down our scope and conduct primary and secondary research to determine our ideal customers. The design of our study intends to imitate the development of a startup where ideas are created from scratch and the final deliverable is a business model plan that shows some sort of traction. Our first major finding is that a sedentary lifestyle can be treated without major challenges in low risk pregnancies. We determined that uncertainty and lack of concise and clear information is one of the main causes of an increased level of sedentary behavior in low risk pregnancies. A significant driver for women to do some sort of activity or exercise stems from feeling supported, which doesn’t necessarily come from their partner or couple, but instead from other women that are going to a similar process as them. There are apps in the market that intend to serve pregnant women; however, there is not one that incorporates a social aspect to achieve their goal. In conclusion, there is opportunity in the market for a socially integrated pregnancy fitness app. The Gleam concept has been consciously developed to decrease sedentary behavior through concise, clear, and reliable information and by encouraging women through a socialization platform.

ContributorsMosier, Jacob Ryan (Co-author) / Flores, Valeria (Co-author) / McCreary, Liam (Co-author) / Byrne, Jared (Thesis director) / Hall, Rick (Committee member) / Silverstein, Taylor (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
148123-Thumbnail Image.png
Description

When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show

When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show that “Moderate videogame play has been found to contribute to emotional stability” (Jones, 2014). This demonstrates that the stress of college can be mitigated by introducing the ability to interact with video games. This same concept has been applied in the workplace, where studies have shown that “Gaming principles such as challenges, competition, rewards and personalization keep employees engaged and learning” (Clark, 2020). This means that if we manage to gamify the college experience, students will be more engaged which will increase and stabilize the retention rate of colleges which utilize this type of experience. Gaming allows students to connect with their peers in a casual environment while also allowing them to find resources around campus and find new places to eat and relax. We plan to gamify the college experience by introducing augmented reality in the form of an app. Augmented reality is “. . . a technology that combines virtual information with the real world” (Chen, 2019). College students will be able to utilize the resources and amenities available to them on campus while completing quests that help them within the application. This demonstrates the ability for video games to engage students using artificial tasks but real actions and experiences which help them feel more connected to campus. Our Founders Lab team has developed and tested an AR application that can be used to connect students with their campus and the resources available to them.

ContributorsKlein, Jonathan (Co-author) / Rangarajan, Padmapriya (Co-author) / Li, Shimei (Co-author) / Byrne, Jared (Thesis director) / Pierce, John (Committee member) / School of International Letters and Cultures (Contributor) / Department of Management and Entrepreneurship (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

This thesis will bring together students to engage in entrepreneurship by finding, measuring and sharing strategic market opportunities. From a student’s perspective, it will take a deep dive into the world of startup ecosystems, markets and trends utilizing both qualitative and quantitative market research techniques. The information gathered has been

This thesis will bring together students to engage in entrepreneurship by finding, measuring and sharing strategic market opportunities. From a student’s perspective, it will take a deep dive into the world of startup ecosystems, markets and trends utilizing both qualitative and quantitative market research techniques. The information gathered has been curated into a productive, meaningful manner, through a report titled “The State of Startups: A Student Perspective.” <br/> The first key theme of this thesis is that market intelligence can be a powerful tool. The second key theme is the power of knowledge implementation towards competitive strategies. The first section of the thesis will focus on identifying and understanding the current “startup” landscape as a basis on which to build strategic and impactful business decisions. This will be accomplished as the team conducts a landscape analysis focused on the student perspective of the student-based North American “entrepreneurial” ecosystem. The second section of the thesis will focus specifically on the personal experiences of student startup founders. This will be accomplished through the analysis of interviews with founders of the startups researched from the first section of the thesis. This will provide us with a direct insight into the student perspective of the student-based North American “entrepreneurial” ecosystem.

ContributorsMinic, Jacob Michael (Co-author) / Callahan, Ryan (Co-author) / Rudick, Justin (Co-author) / Forshey, Cecilia (Co-author) / Hybert, Jacob (Co-author) / Byrne, Jared (Thesis director) / Olsen, Douglas (Committee member) / Curtiss, Ian (Committee member) / Department of Information Systems (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05