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- All Subjects: Technology
- Creators: School of Mathematical and Statistical Sciences
- Creators: Harrington Bioengineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
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
Military personnel are affected by muscle fatigue during the various missions and training regimens for their work. Muscle fatigue is caused by the overuse and lack of nutrients to muscles. When a soldier is fatigued, they are unable to perform at their maximum potential and are also more susceptible to injury. For military personnel to save time and money as well as become more efficient within the missions they deploy soldiers, muscle fatigue should be predicted. Predicting fatigue will allow for a reduced rate of negative exercise-related impacts. This means that soldiers will be able to avoid potential life threatening situations they encounter due to the muscle fatigue. The newest technology in wearable devices includes clothing that incorporates heart rate monitors, breathing rate and breathing depth sensors, and a database that converts this information into the amount of calories burned during a workout. Fatigue can be tracked and predicted in the military using wearable clothing with activity sensors, preventing further injury to the soldiers and optimizing performance output at all times. For military personnel, the ability to predict fatigue using this technology would be beneficial to the soldiers and the military as a whole.
ContributorsFalk, Brady Thomas (Author) / Lockhart, Thurmon (Thesis director) / Williams, Deborah (Committee member) / Harrington Bioengineering 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
For many, a long-distance hike on the 2,650+ mile Pacific Crest Trail (PCT) is the adventure of a lifetime. The federally designated National Scenic Trail passes through 48 Wilderness Areas in California, Washington, and Oregon on its way from Mexico to Canada. The trail experience on the PCT has been changing rapidly over the last 20 years due to two main factors: a four-fold increase in hikers attempting the whole trail each season; and hikers’ rapid adoption of digital technology like smartphones, GPS, and satellite messengers. Through a literature review and accompanying hiker survey, this study aimed to determine how these two factors have combined to alter the trail experience. Despite increased traffic on the trail, hikers appear to still be able to find ample solitude and a feeling of escape from society, and they reported being more likely to form lasting friendships as part of a “trail family”. However, increased traffic has altered many of the sensitive natural landscapes along the trail, contributed to the retirement of some iconic “trail angels” and led to increased conflict between subcultures within the community. Digital technology usage, particularly the use of smartphones and GPS-capable mapping apps, seems to be linked to decreased feelings of solitude, self-sufficiency, and escape. However, digital devices have helped democratize long-distance hiking by simplifying the logistics of long-distance hikes. Users of the devices also did not report reduced feelings of freedom or challenge from their hikes. Moreover, device users still felt that they were disconnecting with technology when hiking on the trail. Acknowledging both positive and negative effects of the changing trail experience, hikers can make more informed decisions about how to mitigate the negative impacts and maximize the positive impacts on the aspects of the trail experience they care the most about.
ContributorsDeSimone, Dante (Author) / Shaeffer, Duncan (Thesis director) / Schmidt, Peter (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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
Smartphones have become an integral component of lifestyles worldwide, acting as mobile computers capable of life organization. They remain the most quickly cycled consumer electronic, owned for no more than 3 years on average. Individuals continue to upgrade their smartphones quickly, stemming from the desire for more power and better features. In 2016, there were 1.15 billion smartphone upgrades, resulting in a growing used smartphone market valued at \$18 billion. Individuals continue to invest time and effort to sell their smartphone, receiving payment of less than market value. In regards to value-minded users with solidified schedules, I created Trusted Trade-in. This startup provides the bustling middle class with the ability to upgrade their smartphone in an efficient and valuable manner. Compared to current solutions, Trusted Trade-in offers an all-in-one upgrade system. The creation of this startup involved the complete creation of a business model in addition to the coding of a responsive website. An online-based business, customers will be able to visit the Trusted Trade-in website and be given the options to trade-in or trade-up. Competing against Craigslist, eBay and Verizon, Trusted Trade-in features a combined smartphone resale and upgrade process. If the decision is made to trade-in, the customer will be quoted for their current smartphone according to specific physical criteria. The trade-up option will request the same information from the customer and allow them to select a new model for their upgrade. This exciting and innovative marketplace will completely transform the way people upgrade their smartphones through financial and time-based savings.
ContributorsWoods, Quintin Delane (Author) / Sebold, Brent (Thesis director) / Lin, Elva S. Y. (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The study tested the parameterized neural ordinary differential equation (PNODE) framework with a physical system exhibiting only advective phenomenon. Existing deep learning methods have difficulty learning multiple dynamic, continuous time processes. PNODE encodes the input data and initial parameter into a set of reduced states within the latent space. Then the reduced states are fitted to a system of ordinary differential equations. The outputs from the model are then decoded back to the data space for a desired input parameter and time. The application of the PNODE formalism to different types of physical systems is important to test the methods robustness. The linear advection data was generated through a high-fidelity numerical tool for multiple velocity parameters. The PNODE code was modified for the advection dataset, whose temporal domain and spatial discretization varied from the original study configuration. The L2 norm between the reconstruction and surrogate model and the reconstruction plots were used to analyze the PNODE model performance. The model reconstructions presented mixed results. For a temporal domain of 20-time units, where multiple advection cycles were completed for each advection speed, the reconstructions did not agree with the surrogate model. For a reduced temporal domain of 5-time units, the reconstructions and surrogate models were in close agreement. Near the end of the temporal domain, deviations occurred likely resulting from the accumulation of numerical errors. Note, over the 5-time units, smaller advection speed parameters were unable to complete a cycle. The behavior for the 20-time units highlighted potential issues with imbalanced datasets and repeated features. The 5-time unit model illustrates PNODEs adaptability to this class of problems when the dataset is better posed.
ContributorsReithal, Richard Robert (Author) / Kim, Jeonglae (Thesis director) / Lee, Kookjin (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-12
Description
Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.
ContributorsMallya, Aatmik (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05