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- Creators: Barrett, The Honors College
Description
Geology and its tangential studies, collectively known and referred to in this thesis as geosciences, have been paramount to the transformation and advancement of society, fundamentally changing the way we view, interact and live with the surrounding natural and built environment. It is important to recognize the value and importance of this interdisciplinary scientific field while reconciling its ties to imperial and colonizing extractive systems which have led to harmful and invasive endeavors. This intersection among geosciences, (environmental) justice studies, and decolonization is intended to promote inclusive pedagogical models through just and equitable methodologies and frameworks as to prevent further injustices and promote recognition and healing of old wounds. By utilizing decolonial frameworks and highlighting the voices of peoples from colonized and exploited landscapes, this annotated syllabus tackles the issues previously described while proposing solutions involving place-based education and the recentering of land within geoscience pedagogical models. (abstract)
ContributorsReed, Cameron E (Author) / Richter, Jennifer (Thesis director) / Semken, Steven (Committee member) / School of Earth and Space Exploration (Contributor, Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The ASU COVID-19 testing lab process was developed to operate as the primary testing site for all ASU staff, students, and specified external individuals. Tests are collected at various collection sites, including a walk-in site at the SDFC and various drive-up sites on campus; analysis is conducted on ASU campus and results are distributed virtually to all patients via the Health Services patient portal. The following is a literature review on past implementations of various process improvement techniques and how they can be applied to the ABCTL testing process to achieve laboratory goals. (abstract)
ContributorsKrell, Abby Elizabeth (Co-author) / Bruner, Ashley (Co-author) / Ramesh, Frankincense (Co-author) / Lewis, Gabriel (Co-author) / Barwey, Ishna (Co-author) / Myers, Jack (Co-author) / Hymer, William (Co-author) / Reagan, Sage (Co-author) / Compton, Carolyn (Thesis director) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Caches have long been used to reduce memory access latency. However, the increased complexity of cache coherence brings significant challenges in processor design as the number of cores increases. While making caches scalable is still an important research problem, some researchers are exploring the possibility of a more power-efficient SRAM called scratchpad memories or SPMs. SPMs consume significantly less area, and are more energy-efficient per access than caches, and therefore make the design of on-chip memories much simpler. Unlike caches, which fetch data from memories automatically, an SPM requires explicit instructions for data transfers. SPM-only architectures are thus named as software managed manycore (SMM), since the data movements of such architectures rely on software. SMM processors have been widely used in different areas, such as embedded computing, network processing, or even high performance computing. While SMM processors provide a low-power platform, the hardware alone does not guarantee power efficiency, if applications on such processors deliver low performance. Efficient software techniques are therefore required. A big body of management techniques for SMM architectures are compiler-directed, as inserting data movement operations by hand forces programmers to trace flow of data, which can be error-prone and sometimes difficult if not impossible. This thesis develops compiler-directed techniques to manage data transfers for embedded applications on SMMs efficiently. The techniques analyze and find out the proper program points and insert data movement instructions accordingly. The techniques manage code, stack and heap data of applications, and reduce execution time by 14%, 52% and 80% respectively compared to their predecessors on typical embedded applications. On top of managing local data, a technique is also developed for shared data in SMM architectures. Experimental results show it achieves more than 2X speedup than the previous technique on average.
ContributorsCai, Jian (Author) / Shrivastava, Aviral (Thesis advisor) / Wu, Carole (Committee member) / Ren, Fengbo (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2017
Description
With the end of Dennard scaling and Moore's law, architects have moved towards
heterogeneous designs consisting of specialized cores to achieve higher performance
and energy efficiency for a target application domain. Applications of linear algebra
are ubiquitous in the field of scientific computing, machine learning, statistics,
etc. with matrix computations being fundamental to these linear algebra based solutions.
Design of multiple dense (or sparse) matrix computation routines on the
same platform is quite challenging. Added to the complexity is the fact that dense
and sparse matrix computations have large differences in their storage and access
patterns and are difficult to optimize on the same architecture. This thesis addresses
this challenge and introduces a reconfigurable accelerator that supports both dense
and sparse matrix computations efficiently.
The reconfigurable architecture has been optimized to execute the following linear
algebra routines: GEMV (Dense General Matrix Vector Multiplication), GEMM
(Dense General Matrix Matrix Multiplication), TRSM (Triangular Matrix Solver),
LU Decomposition, Matrix Inverse, SpMV (Sparse Matrix Vector Multiplication),
SpMM (Sparse Matrix Matrix Multiplication). It is a multicore architecture where
each core consists of a 2D array of processing elements (PE).
The 2D array of PEs is of size 4x4 and is scheduled to perform 4x4 sized matrix
updates efficiently. A sequence of such updates is used to solve a larger problem inside
a core. A novel partitioned block compressed sparse data structure (PBCSC/PBCSR)
is used to perform sparse kernel updates. Scalable partitioning and mapping schemes
are presented that map input matrices of any given size to the multicore architecture.
Design trade-offs related to the PE array dimension, size of local memory inside a core
and the bandwidth between on-chip memories and the cores have been presented. An
optimal core configuration is developed from this analysis. Synthesis results using a 7nm PDK show that the proposed accelerator can achieve a performance of upto
32 GOPS using a single core.
heterogeneous designs consisting of specialized cores to achieve higher performance
and energy efficiency for a target application domain. Applications of linear algebra
are ubiquitous in the field of scientific computing, machine learning, statistics,
etc. with matrix computations being fundamental to these linear algebra based solutions.
Design of multiple dense (or sparse) matrix computation routines on the
same platform is quite challenging. Added to the complexity is the fact that dense
and sparse matrix computations have large differences in their storage and access
patterns and are difficult to optimize on the same architecture. This thesis addresses
this challenge and introduces a reconfigurable accelerator that supports both dense
and sparse matrix computations efficiently.
The reconfigurable architecture has been optimized to execute the following linear
algebra routines: GEMV (Dense General Matrix Vector Multiplication), GEMM
(Dense General Matrix Matrix Multiplication), TRSM (Triangular Matrix Solver),
LU Decomposition, Matrix Inverse, SpMV (Sparse Matrix Vector Multiplication),
SpMM (Sparse Matrix Matrix Multiplication). It is a multicore architecture where
each core consists of a 2D array of processing elements (PE).
The 2D array of PEs is of size 4x4 and is scheduled to perform 4x4 sized matrix
updates efficiently. A sequence of such updates is used to solve a larger problem inside
a core. A novel partitioned block compressed sparse data structure (PBCSC/PBCSR)
is used to perform sparse kernel updates. Scalable partitioning and mapping schemes
are presented that map input matrices of any given size to the multicore architecture.
Design trade-offs related to the PE array dimension, size of local memory inside a core
and the bandwidth between on-chip memories and the cores have been presented. An
optimal core configuration is developed from this analysis. Synthesis results using a 7nm PDK show that the proposed accelerator can achieve a performance of upto
32 GOPS using a single core.
ContributorsAnimesh, Saurabh (Author) / Chakrabarti, Chaitali (Thesis advisor) / Brunhaver, John (Committee member) / Ren, Fengbo (Committee member) / Arizona State University (Publisher)
Created2018
Description
Information forensics and security have come a long way in just a few years thanks to the recent advances in biometric recognition. The main challenge remains a proper design of a biometric modality that can be resilient to unconstrained conditions, such as quality distortions. This work presents a solution to face and ear recognition under unconstrained visual variations, with a main focus on recognition in the presence of blur, occlusion and additive noise distortions.
First, the dissertation addresses the problem of scene variations in the presence of blur, occlusion and additive noise distortions resulting from capture, processing and transmission. Despite their excellent performance, ’deep’ methods are susceptible to visual distortions, which significantly reduce their performance. Sparse representations, on the other hand, have shown huge potential capabilities in handling problems, such as occlusion and corruption. In this work, an augmented SRC (ASRC) framework is presented to improve the performance of the Spare Representation Classifier (SRC) in the presence of blur, additive noise and block occlusion, while preserving its robustness to scene dependent variations. Different feature types are considered in the performance evaluation including image raw pixels, HoG and deep learning VGG-Face. The proposed ASRC framework is shown to outperform the conventional SRC in terms of recognition accuracy, in addition to other existing sparse-based methods and blur invariant methods at medium to high levels of distortion, when particularly used with discriminative features.
In order to assess the quality of features in improving both the sparsity of the representation and the classification accuracy, a feature sparse coding and classification index (FSCCI) is proposed and used for feature ranking and selection within both the SRC and ASRC frameworks.
The second part of the dissertation presents a method for unconstrained ear recognition using deep learning features. The unconstrained ear recognition is performed using transfer learning with deep neural networks (DNNs) as a feature extractor followed by a shallow classifier. Data augmentation is used to improve the recognition performance by augmenting the training dataset with image transformations. The recognition performance of the feature extraction models is compared with an ensemble of fine-tuned networks. The results show that, in the case where long training time is not desirable or a large amount of data is not available, the features from pre-trained DNNs can be used with a shallow classifier to give a comparable recognition accuracy to the fine-tuned networks.
First, the dissertation addresses the problem of scene variations in the presence of blur, occlusion and additive noise distortions resulting from capture, processing and transmission. Despite their excellent performance, ’deep’ methods are susceptible to visual distortions, which significantly reduce their performance. Sparse representations, on the other hand, have shown huge potential capabilities in handling problems, such as occlusion and corruption. In this work, an augmented SRC (ASRC) framework is presented to improve the performance of the Spare Representation Classifier (SRC) in the presence of blur, additive noise and block occlusion, while preserving its robustness to scene dependent variations. Different feature types are considered in the performance evaluation including image raw pixels, HoG and deep learning VGG-Face. The proposed ASRC framework is shown to outperform the conventional SRC in terms of recognition accuracy, in addition to other existing sparse-based methods and blur invariant methods at medium to high levels of distortion, when particularly used with discriminative features.
In order to assess the quality of features in improving both the sparsity of the representation and the classification accuracy, a feature sparse coding and classification index (FSCCI) is proposed and used for feature ranking and selection within both the SRC and ASRC frameworks.
The second part of the dissertation presents a method for unconstrained ear recognition using deep learning features. The unconstrained ear recognition is performed using transfer learning with deep neural networks (DNNs) as a feature extractor followed by a shallow classifier. Data augmentation is used to improve the recognition performance by augmenting the training dataset with image transformations. The recognition performance of the feature extraction models is compared with an ensemble of fine-tuned networks. The results show that, in the case where long training time is not desirable or a large amount of data is not available, the features from pre-trained DNNs can be used with a shallow classifier to give a comparable recognition accuracy to the fine-tuned networks.
ContributorsMounsef, Jinane (Author) / Karam, Lina (Thesis advisor) / Papandreou-Suppapola, Antonia (Committee member) / Li, Baoxin (Committee member) / Ren, Fengbo (Committee member) / Arizona State University (Publisher)
Created2018
Description
For as long as humans have been working, they have been looking for ways to get that work done better, faster, and more efficient. Over the course of human history, mankind has created innumerable spectacular inventions, all with the goal of making the economy and daily life more efficient. Today, innovations and technological advancements are happening at a pace like never seen before, and technology like automation and artificial intelligence are poised to once again fundamentally alter the way people live and work in society. Whether society is prepared or not, robots are coming to replace human labor, and they are coming fast. In many areas artificial intelligence has disrupted entire industries of the economy. As people continue to make advancements in artificial intelligence, more industries will be disturbed, more jobs will be lost, and entirely new industries and professions will be created in their wake. The future of the economy and society will be determined by how humans adapt to the rapid innovations that are taking place every single day. In this paper I will examine the extent to which automation will take the place of human labor in the future, project the potential effect of automation to future unemployment, and what individuals and society will need to do to adapt to keep pace with rapidly advancing technology. I will also look at the history of automation in the economy. For centuries humans have been advancing technology to make their everyday work more productive and efficient, and for centuries this has forced humans to adapt to the modern technology through things like training and education. The thesis will additionally examine the ways in which the U.S. education system will have to adapt to meet the demands of the advancing economy, and how job retraining programs must be modernized to prepare workers for the changing economy.
ContributorsCunningham, Reed P. (Author) / DeSerpa, Allan (Thesis director) / Haglin, Brett (Committee member) / School of International Letters and Cultures (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Businesses stand to face many uncertainties from the moment they start up to every moment in between. A business can try to recognize them and plan ahead, react to them as they occur, or be rocked by a black swan they never saw coming. How a business deals with unforeseen events can increase its potential for success or failure. With this in mind, there is no better bridge between the here and now and the future than planning for change in order to move a company toward preparing for change, adapting to change and achieving optimal results. Interested in taking a step toward the digital age, Alpha Homes Management, Inc. (Alpha Homes) sought our help to explore ideas and options to take their company to a new level. This Barrett Creative Project was centered on designing a system for Alpha Homes that will replace their outdated paper-based system with a more digital one. This aligns with the project also featured as a capstone project as required by the information technology degree expectations. In supplement to the capstone, and for the Barrett Creative Project, the final product was presented to the owners of Alpha Homes Management, Inc. to be utilized by the business. The end goal is to provide a platform which provides a paperless environment for documentation and bring the company a step closer to having a robust internet presence. Now that the web-based application product has been created and presented, the testing phase can now begin to evaluate its efficacy.
ContributorsBrice-Nash, Tristan (Co-author) / Alfawzan, Mohammad (Co-author) / Doheny, Damien (Thesis director) / Rodriguez, Carlos (Committee member) / Information Technology (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
An ethical dilemma is not a matter of “right” versus “wrong,” but rather it is a situation of conflicting values. A common ethical dilemma is that of honesty versus loyalty—is it better to tell the truth, or remain loyal to the company? In the Japanese culture, truth is circumstantial and can vary with different situations. In a way, the Japanese idea of honesty reflects how highly they value loyalty. This overlap of values results in the lack of an ethical dilemma for the Japanese, which creates a new risk for fraud. Without this struggle, a Japanese employee does not have strong justification against committing fraud if it aligns with his values of honesty and loyalty.
This paper looks at the Japanese values relating to honesty and loyalty to show how much these ideas overlap. The lack of a conflict of values creates a risk for fraud, which will be shown through an analysis of the scandals of two Japanese companies, Toshiba and Olympus. These scandals shine light on the complexity of the ethical dilemma for the Japanese employees; since their sense of circumstantial honesty encourages them to lie if it maintains the harmony of the group, there is little stopping them from committing the fraud that their superiors asked them to commit.
In a global economy, understanding the ways that values impact business and decisions is important for both interacting with others and anticipating potential conflicts, including those that may result in or indicate potential red flags for fraud.
This paper looks at the Japanese values relating to honesty and loyalty to show how much these ideas overlap. The lack of a conflict of values creates a risk for fraud, which will be shown through an analysis of the scandals of two Japanese companies, Toshiba and Olympus. These scandals shine light on the complexity of the ethical dilemma for the Japanese employees; since their sense of circumstantial honesty encourages them to lie if it maintains the harmony of the group, there is little stopping them from committing the fraud that their superiors asked them to commit.
In a global economy, understanding the ways that values impact business and decisions is important for both interacting with others and anticipating potential conflicts, including those that may result in or indicate potential red flags for fraud.
ContributorsTabar, Kelly Ann (Author) / Samuelson, Melissa (Thesis director) / Goldman, Alan (Committee member) / WPC Graduate Programs (Contributor) / W.P. Carey School of Business (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper will be exploring a marketing plan for a Kpop Fan artist, Jennifer Lee. Kpop is a genre of music originating from South Korea that provides a whole-package entertainment. Fan artists are producers who create produce for the consumption and purchase of other Kpop fans. The paper will consider segmentation and the products and platforms that best target them in order to maximize revenue. A survey was performed with a sample size of 314 participants to find out consumer behavior and preference as well as producer situation. Consumers come from both the United States and abroad. Customers come directly and almost exclusively from followers. Therefore, increasing the number of followers on Instagram is essential to increasing revenue. Jennifer has time, resource, and ability constraints, while the market has limited potential. The conclusion is that Jennifer should become more organized as a business. To grow her following, she should cater more towards the most popular fandoms (BTS), make art tutorials, consider collaborations, and better inform followers of her products/services available for purchase. The social media platforms key to marketing Jennifer's products are Instagram and Twitter. Other platforms to be used to increase exposure are Tumblr, Amino Apps, DeviantArt, Reddit, and YouTube. She must also declutter all of these virtual storefronts of unnecessary content to varying degrees in order to build ease of access and a trustworthy brand image. The best platforms for transaction is a personal store, RedBubble (a website that allows users to sell a variety of products with their uploaded images printed onto them), Patreon, and in-person at conventions.
ContributorsXu, Everest Christine (Author) / Eaton, Kathryn (Thesis director) / Ingram-Waters, Mary (Committee member) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In recent years, sex trafficking awareness and intervention have skyrocketed in the United States. The 2016 Polaris Hotline Statistics Sheet reports a drastic increase of reported sex trafficking cases over the span of four years, with only 3,409 cases of human trafficking in 2012 and 8,042 in 2016, 73% of which were specifically sex trafficking cases (Polaris Project, 2016). The incidence of sex trafficking has not increased, but rather, attention to sex trafficking and implementation of legislation has increased awareness and reporting (Farrell et al., 2012). While this rise in public awareness of sex trafficking has positively impacted victim identification, there has not been an increase in convicting sex traffickers (Polaris Project, 2016). According to the 2016 Trafficking in Persons Report, 3,000 federal investigations that involved human trafficking, the majority of which specifically involved sex trafficking, were opened in 2015. Of these federal investigations, only 10% led to case prosecutions. Analyzing the relationship of law enforcement, specifically vice units, and victims of sex trafficking is just one of the many ways to address this complex issue. This study consisted of a qualitative analysis of the makeup, training, and policing methods of vice units nationwide. It further aimed to determine the vice officer perceptions regarding the elements that make sex trafficking cases convictable.
ContributorsPradhan, Krishnaa (Author) / Maienschein, Jane (Thesis director) / Kinzig, Ann (Committee member) / Kim, Grace (Committee member) / Barrett, The Honors College (Contributor)
Created2018-05