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- Creators: School of Mathematical and Statistical Sciences
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Description
The purpose of our research was to develop recommendations and/or strategies for Company A's data center group in the context of the server CPU chip industry. We used data collected from the International Data Corporation (IDC) that was provided by our team coaches, and data that is accessible on the internet. As the server CPU industry expands and transitions to cloud computing, Company A's Data Center Group will need to expand their server CPU chip product mix to meet new demands of the cloud industry and to maintain high market share. Company A boasts leading performance with their x86 server chips and 95% market segment share. The cloud industry is dominated by seven companies Company A calls "The Super 7." These seven companies include: Amazon, Google, Microsoft, Facebook, Alibaba, Tencent, and Baidu. In the long run, the growing market share of the Super 7 could give them substantial buying power over Company A, which could lead to discounts and margin compression for Company A's main growth engine. Additionally, in the long-run, the substantial growth of the Super 7 could fuel the development of their own design teams and work towards making their own server chips internally, which would be detrimental to Company A's data center revenue. We first researched the server industry and key terminology relevant to our project. We narrowed our scope by focusing most on the cloud computing aspect of the server industry. We then researched what Company A has already been doing in the context of cloud computing and what they are currently doing to address the problem. Next, using our market analysis, we identified key areas we think Company A's data center group should focus on. Using the information available to us, we developed our strategies and recommendations that we think will help Company A's Data Center Group position themselves well in an extremely fast growing cloud computing industry.
ContributorsJurgenson, Alex (Co-author) / Nguyen, Duy (Co-author) / Kolder, Sean (Co-author) / Wang, Chenxi (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Department of Management (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (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
In an effort to gauge on-campus resident's satisfaction with services provided by Century Link and the University Technology Office as well as understand the resident's technology usage habits, the Performance Based Research Studies Group at ASU conducted a survey to collect the data needed to initiate improvements. Unlike previous years, the 2015 edition of the survey was distributed more efficiently by engaging University Housing staff members (those who work closest with the residents). The result was a 288% increase in responses from the previous year, totaling 2352 respondents and a 167% increase in the number of Residential Halls surveyed, totaling 24. As a primary concern, on a scale of zero to five, the average Internet satisfaction rating was 2.42. In the comments section residents reported issues with the reliability and speed of the ASU networks. It was further determined that residents were dissatisfied with the television services with an average satisfaction rating of 2.91; and the vast majority of comments regarding television services demanding that the ESPN channels be provided. In addition to the metrics on resident satisfaction, it was found that the majority of on-campus residents do not utilize hard-wired ports. Based on the information gathered from this survey, it is recommended that the University Technology Office: 1) focus efforts on upgrading, expanding, and improving the existing ASU networks in particular the reliability and speed of those networks, 2) invest in a broader channel line-up to at minimum provide the ESPN channels, and 3) start an awareness campaign to educate residents on the usage of hard wired ports with the goal of increasing hard wired port usage. As a corollary to information gathered from the survey, it is possible to begin building technology usage profiles on each building and even building such profiles on each residential college and academic unit to better understand the clientele and adapt the services a necessary.
ContributorsMcculloch, John Patrick (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Accurate pose initialization and pose estimation are crucial requirements in on-orbit space assembly and various other autonomous on-orbit tasks. However, pose initialization and pose estimation are much more difficult to do accurately and consistently in space. This is primarily due to not only the variable lighting conditions present in space, but also the power requirements mandated by space-flyable hardware. This thesis investigates leveraging a deep learning approach for monocular one-shot pose initialization and pose estimation. A convolutional neural network was used to estimate the 6D pose of an assembly truss object. This network was trained by utilizing synthetic imagery generated from a simulation testbed. Furthermore, techniques to quantify model uncertainty of the deep learning model were investigated and applied in the task of in-space pose estimation and pose initialization. The feasibility of this approach on low-power computational platforms was also tested. The results demonstrate that accurate pose initialization and pose estimation can be conducted using a convolutional neural network. In addition, the results show that the model uncertainty can be obtained from the network. Lastly, the use of deep learning for pose initialization and pose estimation in addition with uncertainty quantification was demonstrated to be feasible on low-power compute platforms.
ContributorsKailas, Siva Maneparambil (Author) / Ben Amor, Heni (Thesis director) / Detry, Renaud (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In shotgun proteomics, liquid chromatography coupled to tandem mass spectrometry
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
ContributorsWhyte, Cameron Stafford (Author) / Suren, Jayasuriya (Thesis director) / Gil, Speyer (Committee member) / Patrick, Pirrotte (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Convolutional neural networks boast a myriad of applications in artificial intelligence, but one of the most common uses for such networks is image extraction. The ability of convolutional layers to extract and combine data features for the purpose of image analysis can be leveraged for pose estimation on an object - detecting the presence and attitude of corners and edges allows a convolutional neural network to identify how an object is positioned. This task can assist in working to grasp an object correctly in robotics applications, or to track an object more accurately in 3D space. However, the effectiveness of pose estimation may change based on properties of the object; the pose of a complex object, complexity being determined by internal occlusions, similar faces, etcetera, can be difficult to resolve.
This thesis is part of a collaboration between ASU’s Interactive Robotics Laboratory and NASA’s Jet Propulsion Laboratory. In this thesis, the training pipeline from Sharma’s paper “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks” was modified to perform pose estimation on a complex object - specifically, a segment of a hollow truss. After initial attempts to replicate the architecture used in the paper and train solely on synthetic images, a combination of synthetic dataset generation and transfer learning on an ImageNet-pretrained AlexNet model was implemented to mitigate the difficulty of gathering large amounts of real-world data. Experimentation with pose estimation accuracy and hyperparameters of the model resulted in gradual test accuracy improvement, and future work is suggested to improve pose estimation for complex objects with some form of rotational symmetry.
This thesis is part of a collaboration between ASU’s Interactive Robotics Laboratory and NASA’s Jet Propulsion Laboratory. In this thesis, the training pipeline from Sharma’s paper “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks” was modified to perform pose estimation on a complex object - specifically, a segment of a hollow truss. After initial attempts to replicate the architecture used in the paper and train solely on synthetic images, a combination of synthetic dataset generation and transfer learning on an ImageNet-pretrained AlexNet model was implemented to mitigate the difficulty of gathering large amounts of real-world data. Experimentation with pose estimation accuracy and hyperparameters of the model resulted in gradual test accuracy improvement, and future work is suggested to improve pose estimation for complex objects with some form of rotational symmetry.
ContributorsDsouza, Susanna Roshini (Author) / Ben Amor, Hani (Thesis director) / Maneparambil, Kailasnath (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This project looks at the impact that the internet has had on society, and how it has shaped the way that digitally native generations live their lives. More specifically, this thesis showcases what it means for younger generations to be digitally native and how engaging with technology while growing up affects the way that these individuals experience contemporary adolescence. Generation X is said to be the last group of people to experience life before the spread of the personal computer and internet access. Newer generations, such as Generation Z, have grown up having constant and easy access to the internet, all of the information it encompasses, and its additional functions. This access has shaped much of the generation as individuals as well as society as a whole. It can be argued that the human experience has been fundamentally different for those born after the creation of the internet and the rapid increase in accessible technology that followed. Through an interview with a participant from Generation X, I will showcase the transformative role that the internet and technology has played in major life events for a digitally native individual compared to that of individuals from older generations. As a member of Generation Z, I will compare my personal narrative regarding ten different life events occurring between the ages of five to 25 that I feel are common and impactful to the narrative a of non-digitally native individual. I expect to see that the internet and the creation of cyber culture that we see through social media has enhanced many of the defining events for younger generations growing up in some positive ways as well as some negative ways. Thus, growing up only knowing the internet and its purposes has altered the way that our experiences play out as we age, for good and for bad.
ContributorsTomchak, Marissa Janine (Author) / Ingram-Waters, Mary (Thesis director) / Brian, Jennifer (Committee member) / Department of Psychology (Contributor) / School of Politics and Global Studies (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Early adolescence is a pivotal stage of social and emotional development. Socialization traditionally occurs in person, but social interactions via technology (e.g., social media, video games) have grown in popularity. However, little research has been conducted on how early adolescents interact with technology and how these interactions relate to their socialization as well as other factors such as reading habits or academic achievement. Seventh and eighth grade students (n = 719) completed a survey that captured information about their technology use, their academic habits and performance, and extracurricular involvement. It was hypothesized that those involved in more extracurricular activities would use the internet more socially and that internet use would be negatively correlated to both academic performance and recreational reading. Responses indicated that a majority of students have access to technology (e.g. internet, computers, television, gaming consoles, and tablets) in their homes. Social media use differed drastically between platforms. Analyses indicated a relation between amount of extracurricular activities on social television watching and social internet use, but not on social gaming. A significant negative correlation was found between recreational reading and time spent socializing online, but there was no significant effect of these factors on academic performance. Thus, hypotheses were partially supported by the relation between amount of extracurriculars and social internet use and the negative correlation between time spent socializing online and recreational reading.
ContributorsHorner, Kate Elizabeth (Author) / McNamara, Danielle (Thesis director) / McCarthy, Kathryn (Committee member) / Davis, Mary (Committee member) / Division of Teacher Preparation (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Smart cities ""utilize information and communication technologies with the aim to increase the life quality of their inhabitants while providing sustainable development"". The Internet of Things (IoT) allows smart devices to communicate with each other using wireless technology. IoT is by far the most important component in the development of smart cities. Company X is a leader in the semiconductor industry looking to grow its revenue in the IoT space. This thesis will address how Company X can deliver IoT solutions to government municipalities with the goal of simultaneously increasing revenue through value-added engagement and decreasing spending by more efficiently managing infrastructure upgrades.
ContributorsJiang, Yichun (Co-author) / Davidoff, Eric (Co-author) / Dawoud, Mariam (Co-author) / Rodenbaugh, Ryan (Co-author) / Sinclair, Brynn (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Psychology (Contributor) / School of Sustainability (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Body-worn camera technology is a relatively new player in the field of criminal justice. As much as they are being reported on or discussed, in reality, body-worn cameras have not been in use long enough to have strong empirical support. Recent studies outlined some of the perceived benefits and costs of the body-worn cameras. Research has been done on both officer and citizen perceptions of the cameras, but little has been done in regards to other stakeholders, especially those in the criminal justice system. This study takes 13 interviews of community and criminal justice stakeholders in Tempe, Arizona and examines trends to identify unifying themes. The study found that 11 out of 13 stakeholders believed that the positives of the body-worn cameras outweighed the negatives. There was agreement among the parties that the strongest benefit of the cameras would be the transparency that it provides police departments, while most regarded the largest negative to be a lack of available resources to deal with the amount of data produced. As this is a small qualitative dataset, further research should be conducted about stakeholder perceptions in other cities, as well as solutions to some of the concerns raised by Tempe interviewees.
ContributorsArenas, Lauren (Author) / White, Michael (Thesis director) / Gaub, Janne (Committee member) / Department of Psychology (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12