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- Genre: Masters Thesis
- Creators: Bansal, Ajay
- Creators: Henderson, Christopher
Description
Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
ContributorsHenderson, Christopher (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2018
Description
Mobile data collection (MDC) applications have been growing in the last decade
especially in the field of education and research. Although many MDC applications are
available, almost all of them are tailor-made for a very specific task in a very specific
field (i.e. health, traffic, weather forecasts, …etc.). Since the main users of these apps are
researchers, physicians or generally data collectors, it can be extremely challenging for
them to make adjustments or modifications to these applications given that they have
limited or no technical background in coding. Another common issue with MDC
applications is that its functionalities are limited only to data collection and storing. Other
functionalities such as data visualizations, data sharing, data synchronization and/or data updating are rarely found in MDC apps.
This thesis tries to solve the problems mentioned above by adding the following
two enhancements: (a) the ability for data collectors to customize their own applications
based on the project they’re working on, (b) and introducing new tools that would help
manage the collected data. This will be achieved by creating a Java standalone
application where data collectors can use to design their own mobile apps in a userfriendly Graphical User Interface (GUI). Once the app has been completely designed
using the Java tool, a new iOS mobile application would be automatically generated
based on the user’s input. By using this tool, researchers now are able to create mobile
applications that are completely tailored to their needs, in addition to enjoying new
features such as visualize and analyze data, synchronize data to the remote database,
share data with other data collectors and update existing data.
especially in the field of education and research. Although many MDC applications are
available, almost all of them are tailor-made for a very specific task in a very specific
field (i.e. health, traffic, weather forecasts, …etc.). Since the main users of these apps are
researchers, physicians or generally data collectors, it can be extremely challenging for
them to make adjustments or modifications to these applications given that they have
limited or no technical background in coding. Another common issue with MDC
applications is that its functionalities are limited only to data collection and storing. Other
functionalities such as data visualizations, data sharing, data synchronization and/or data updating are rarely found in MDC apps.
This thesis tries to solve the problems mentioned above by adding the following
two enhancements: (a) the ability for data collectors to customize their own applications
based on the project they’re working on, (b) and introducing new tools that would help
manage the collected data. This will be achieved by creating a Java standalone
application where data collectors can use to design their own mobile apps in a userfriendly Graphical User Interface (GUI). Once the app has been completely designed
using the Java tool, a new iOS mobile application would be automatically generated
based on the user’s input. By using this tool, researchers now are able to create mobile
applications that are completely tailored to their needs, in addition to enjoying new
features such as visualize and analyze data, synchronize data to the remote database,
share data with other data collectors and update existing data.
ContributorsAl-Kaf, Zahra M (Author) / Lindquist, Timothy E (Thesis advisor) / Bansal, Srividya (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2016
Description
Alzheimer’s disease (AD), is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. It is the cause of 60% to 70% of cases of dementia. There is growing interest in identifying brain image biomarkers that help evaluate AD risk pre-symptomatically. High-dimensional non-linear pattern classification methods have been applied to structural magnetic resonance images (MRI’s) and used to discriminate between clinical groups in Alzheimers progression. Using Fluorodeoxyglucose (FDG) positron emission tomography (PET) as the pre- ferred imaging modality, this thesis develops two independent machine learning based patch analysis methods and uses them to perform six binary classification experiments across different (AD) diagnostic categories. Specifically, features were extracted and learned using dimensionality reduction and dictionary learning & sparse coding by taking overlapping patches in and around the cerebral cortex and using them as fea- tures. Using AdaBoost as the preferred choice of classifier both methods try to utilize 18F-FDG PET as a biological marker in the early diagnosis of Alzheimer’s . Addi- tional we investigate the involvement of rich demographic features (ApoeE3, ApoeE4 and Functional Activities Questionnaires (FAQ)) in classification. The experimental results on Alzheimer’s Disease Neuroimaging initiative (ADNI) dataset demonstrate the effectiveness of both the proposed systems. The use of 18F-FDG PET may offer a new sensitive biomarker and enrich the brain imaging analysis toolset for studying the diagnosis and prognosis of AD.
ContributorsSrivastava, Anant (Author) / Wang, Yalin (Thesis advisor) / Bansal, Ajay (Thesis advisor) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2017