Matching Items (946)
Filtering by
- Creators: Computer Science and Engineering Program
- Member of: Theses and Dissertations
Description
This study explores the topic of the birth control use of college women, and the factors that influence their decision of whether or not to use contraception consistently. A literature review was performed on Academic Search Premier, SocIndex, Women's Studies International, Pubmed, CINAHL, and ICRW. Interviews were conducted with 7 participants recruited through convenience sampling. The results suggest that low perception of risk, lack of access, and alcohol use are all major influences on women's decisions regarding birth control. A review of current policy was also completed, and potential policy changes are suggested in order to improve college women's consistent contraceptive use.
ContributorsMorehouse, Brittany Anne (Author) / Weitz, Rose (Thesis director) / Durfee, Alesha (Committee member) / School of Politics and Global Studies (Contributor) / School of Social Transformation (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Company X has developed RealSenseTM technology, a depth sensing camera that provides machines the ability to capture three-dimensional spaces along with motion within these spaces. The goal of RealSense was to give machines human-like senses, such as knowing how far away objects are and perceiving the surrounding environment. The key issue for Company X is how to commercialize RealSense's depth recognition capabilities. This thesis addresses the problem by examining which markets to address and how to monetize this technology. The first part of the analysis identified potential markets for RealSense. This was achieved by evaluating current markets that could benefit from the camera's gesture recognition, 3D scanning, and depth sensing abilities. After identifying seven industries where RealSense could add value, a model of the available, addressable, and obtainable market sizes was developed for each segment. Key competitors and market dynamics were used to estimate the portion of the market that Company X could capture. These models provided a forecast of the discounted gross profits that could be earned over the next five years. These forecasted gross profits, combined with an examination of the competitive landscape and synergistic opportunities, resulted in the selection of the three segments thought to be most profitable to Company X. These segments are smart home, consumer drones, and automotive. The final part of the analysis investigated entrance strategies. Company X's competitive advantages in each space were found by examining the competition, both for the RealSense camera in general and other technologies specific to each industry. Finally, ideas about ways to monetize RealSense were developed by exploring various revenue models and channels.
ContributorsDunn, Nicole (Co-author) / Boudreau, Thomas (Co-author) / Kinzy, Chris (Co-author) / Radigan, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / WPC Graduate Programs (Contributor) / Department of Psychology (Contributor) / Department of Finance (Contributor) / School of Accountancy (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Science (Contributor) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In 2012, President Obama presented an executive order, Deferred Action for Childhood Arrivals (DACA), which primarily defers the deportation of unauthorized immigrants who are under the age of 31 and who arrived to the US before the age 16, among other things. This study examines the impact DACA has had on the identity formation, interfamilial relationships, and future plans of 15 Mexican-origin adults (18-27 years old, 47% female) who were approved for DACA in Arizona. Participants were recruited using flyers and the snowball sampling method. Interviews were conducted using a semi-structured interview guide by a bilingual, culturally competent interviewer. The interviews were recorded and then a transcript-based pragmatic, thematic analysis of the interviews was conducted. Findings show that participants who arrive to the U.S. at a younger age (under 5) identify as American, while those who arrive at an older age (over 8) do not feel like they can identify as an American because they spent more time in Mexico and are more attached to their home culture. Physical characteristics also played a factor in whether or not participants felt like they could identify as American. Participants describe their financial responsibility in their families increasing since receiving DACA. They also describe how they are now seen as role models to other undocumented youth in their families. Despite the uncertain future of DACA, these participants continued to have ambitious goals such as becoming lawyers and working at robotics companies. Future studies should include larger sample sizes and formally test theories of identity.
ContributorsLopez, Alejandra (Author) / Diaz McConnell, Eileen (Thesis director) / Martinez, Airin (Committee member) / School of Social Transformation (Contributor) / School of Transborder Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Even at the start of the twenty-first century, gender stereotypes continue to guide how people perceive men and women. Given the power of gender stereotypes, I expect that these stereotypes will constrain how women campaign for positions on state supreme courts. In particular, I expect that women candidates for state supreme court will try to revise potentially damaging stereotypes by detailing their possession of agentic traits, while men candidates for state supreme court will have more flexibility when describing their possession of particular traits. When discussing issues in their campaigns, I expect women to highlight issues that correspond to their stereotypical strengths (i.e., communal issues) since by stressing these issues, the candidates hope to prime issues that may benefit their candidacies. In contrast, I expect male candidates for state supreme court to be less constrained by persisting stereotypes and be equally likely to emphasize communal or competitive issues in their campaigns. To test my expectations, I conduct a content analysis of judicial campaign advertisements among the states holding elections for state supreme court. The evidence I find from my analysis strongly supports my hypothesis. This suggests that women are still confined by gender stereotypes when it comes to campaigning in judicial elections.
ContributorsKahn, Jennifer Gail (Author) / Hoekstra, Valerie (Thesis director) / Fridkin, Kim (Committee member) / School of Social Transformation (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The constant evolution of technology has greatly shifted the way in which we gain knowledge information. This, in turn, has an affect on how we learn. Long gone are the days where students sit in libraries for hours flipping through numerous books to find one specific piece of information. With the advent of Google, modern day students are able to arrive at the same information within 15 seconds. This technology, the internet, is reshaping the way we learn. As a result, the academic integrity policies that are set forth at the college level seem to be outdated, often prohibiting the use of technology as a resource for learning. The purpose of this paper is to explore why exactly these resources are prohibited. By contrasting a subject such as Computer Science with the Humanities, the paper explores the need for the internet as a resource in some fields as opposed to others. Taking a look at the knowledge presented in Computer Science, the course structure, and the role that professors play in teaching this knowledge, this thesis evaluates the epistemology of Engineering subjects. By juxtaposing Computer Science with the less technology reliant humanities subjects, it is clear that one common policy outlining academic integrity does not suffice for an entire university. Instead, there should be amendments made to the policy specific to each subject, in order to best foster an environment of learning at the university level. In conclusion of this thesis, Arizona State University's Academic Integrity Policy is analyzed and suggestions are made to remove ambiguity in the language of the document, in order to promote learning at the university.
ContributorsMohan, Sishir Basavapatna (Author) / Brake, Elizabeth (Thesis director) / Martin, William (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of this project was to construct and write code for a vehicle to take advantage of the benefits of combining stepper motors with mecanum wheels. This process involved building the physical vehicle, designing a custom PCB for the vehicle, writing code for the onboard microprocessor, and implementing motor control algorithms.
ContributorsDavis, Severin Jan (Author) / Burger, Kevin (Thesis director) / Vannoni, Greg (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Abstract
Diagnosing psychogenic non-epileptic seizures (PNES) requires admission to an epilepsy monitoring unit, which is a lengthy and expensive process. Despite the cost of and time commitment to this inpatient evaluation, a definitive diagnosis at the end isn’t always guaranteed. Therefore, predictor variables such as demographic information and psychological testing scores can help improve the accuracy of diagnosing PNES or epilepsy at the end of a patient’s EMU admission. Locke et al. have demonstrated that the SOM scale and SOM-C subscale on the Personality Assessment Inventory (PAI) are the best indicators for predicting PNES diagnosis, with an optimal cut score of T≥70 on both of these scales. The aim of the current study was to determine whether evaluating male and female performance separately on these relevant PAI scales improves the accuracy of diagnosing PNES. The results support the hypothesis, such that male optimal cut scores on the SOM and SOM C scales are T=80 and T=75, respectively, and female optimal cut scores on the SOM and SOM C scales are T=71 and T=72, respectively. Utilizing the results of this study can help clinicians diagnose patients with PNES or epilepsy at the end of EMU evaluation with more certainty.
Diagnosing psychogenic non-epileptic seizures (PNES) requires admission to an epilepsy monitoring unit, which is a lengthy and expensive process. Despite the cost of and time commitment to this inpatient evaluation, a definitive diagnosis at the end isn’t always guaranteed. Therefore, predictor variables such as demographic information and psychological testing scores can help improve the accuracy of diagnosing PNES or epilepsy at the end of a patient’s EMU admission. Locke et al. have demonstrated that the SOM scale and SOM-C subscale on the Personality Assessment Inventory (PAI) are the best indicators for predicting PNES diagnosis, with an optimal cut score of T≥70 on both of these scales. The aim of the current study was to determine whether evaluating male and female performance separately on these relevant PAI scales improves the accuracy of diagnosing PNES. The results support the hypothesis, such that male optimal cut scores on the SOM and SOM C scales are T=80 and T=75, respectively, and female optimal cut scores on the SOM and SOM C scales are T=71 and T=72, respectively. Utilizing the results of this study can help clinicians diagnose patients with PNES or epilepsy at the end of EMU evaluation with more certainty.
ContributorsCorallo, Kelsey Lynn (Author) / Lanyon, Richard (Thesis director) / Knight, George (Committee member) / Karoly, Paul (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / Department of Psychology (Contributor)
Created2015-05
Description
Two primary contexts for the adaptive evolution of bright coloration are competition for mates (i.e. mate choice) and avoiding predator attacks (i.e. warning coloration). Bright animal coloration can be iridescent, in which the surface appears to change color with changing viewing or illumination angle. Bright animal coloration can also be produced by pigments, which do not appear to change color with changing viewing or illumination angle. The Pipevine Swallowtail, Battus philenor, is unique in having both sexual signals and warning coloration that include iridescent and pigment components, both of which are variable in color. The aim of our study was to examine the role genes play in producing this variation, providing us a sense of potential indirect benefits of female choice. We tested the hypothesis that color variation has a genetic component. We predicted that in a full-sib analysis there should be greater variation in the coloration of the sexual and warning signal among families than within families. We reared B. philenor under standard laboratory conditions and analyzed heritability using a full-sib analysis. We collected reflectance measurements for components of the sexual and warning signal iridescence using a spectrophotometer and used CLR (color analysis software) to extract brightness, hue, and chroma values. We used a multivariate ANOVA (IBM SPSS, v. 21) to analyze the warning signal variation, and a generalized linear mixed model (IBM SPSS, v. 21) to analyze the sexual versus warning signal variation in males. A significance value of 0.05 was used for both analyses. Our results indicated a genetic component to coloration, implicating indirect benefits in B. philenor female mate bias. Further research on bright coloration in B. philenor indicates that there may also be direct benefits of female mate choice.
ContributorsOlzer, Rachel Maureen (Co-author) / Raymundo, Andrew (Co-author) / Pegram, Kimberly (Co-author) / Rutowski, Ronald (Co-author, Thesis director) / Pratt, Stephen (Committee member) / Papaj, Daniel (Committee member) / Barrett, The Honors College (Contributor) / School of Social Transformation (Contributor) / School of Social Sciences (Contributor)
Created2014-12
Description
This project was centered around designing a processor model (using the C programming language) based on the Coldfire computer architecture that will run on third party software known as Open Virtual Platforms. The end goal is to have a fully functional processor that can run Coldfire instructions and utilize peripheral devices in the same way as the hardware used in the embedded systems lab at ASU. This project would cut down the substantial amount of time students spend commuting to the lab. Having the processor directly at their disposal would also encourage them to spend more time outside of class learning the hardware and familiarizing themselves with development on an embedded micro-controller. The model will be accurate, fast and reliable. These aspects will be achieved through rigorous unit testing and use of the OVP platform which provides instruction accurate simulations at hundreds of MIPS (million instructions per second) for the specified model. The end product was able to accurately simulate a subset of the Coldfire instructions at very high rates.
ContributorsDunning, David Connor (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-12