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Augmented image classification using image registration techniques

Description

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find…

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.

ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)

Created2011

Company X Digital Education

Description

The digital education market has been expanding rapidly during the last few years, providing ample opportunity in the future. There are many technologies involved in this market, with the most significant being cloud client services, edge computing, and interactive flat panel displays (IFPDs). Combined, these technologies represent ninety-two percent of…

The digital education market has been expanding rapidly during the last few years, providing ample opportunity in the future. There are many technologies involved in this market, with the most significant being cloud client services, edge computing, and interactive flat panel displays (IFPDs). Combined, these technologies represent ninety-two percent of the Serviceable Available Market with IFPDs as the largest opportunity with sixty-nine percent (Company X Data). Cloud computing provides anytime/anywhere services that can be accessed from any device: e-portfolios, content access, and creation. Edge computing processes data closer to its source and the point of service delivery, providing ultra-low latency to help boost efficiency, mainly used for data processing. Lastly, IFPDs are touchscreen display devices ideal for collaborative spaces and meeting rooms. They are a substitute for outdated projector technology and provide better connectivity and built-in software solutions. <br/>We decided to develop a strategy to expand the market share in the IFPD market because it is the largest, and we consider Company X has a significant advantage in this sector. Company X manufactures the processors used in IFPDs and has established good relationships with manufacturers of these devices.

ContributorsPhillips, Maya (Co-author) / Hauck, Tanner (Co-author) / Morales Rincon, Herwin (Thesis director) / Koroli, Eri (Thesis director) / Simonson, Mark (Committee member) / Department of Finance (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

An Assessment of the Edge Computing Market Opportunities for Company X

Description

Edge computing is a new and growing market that Company X has an opportunity to expand their presence. Within this paper, we compare many external research studies to better quantify the Total Addressable Market of the Edge Computing space. Furthermore, we highlight which Segments within Edge Computing have the most…

Edge computing is a new and growing market that Company X has an opportunity to expand their presence. Within this paper, we compare many external research studies to better quantify the Total Addressable Market of the Edge Computing space. Furthermore, we highlight which Segments within Edge Computing have the most opportunities for growth, along with identify a specific market strategy that Company X could do to capture market share within the most opportunistic segment.

ContributorsRaimondi, Ronald Frank (Co-author) / Hamkins, Sean (Co-author) / Gandolfi, Michael (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

An Assessment of the Edge Computing Market Opportunities for Company X

Description

Edge computing is a new and growing market that Company X has an opportunity to expand their presence. Within this paper, we compare many external research studies to better quantify the Total Addressable Market of the Edge Computing space. Furthermore, we highlight which Segments within Edge Computing have the most…

Edge computing is a new and growing market that Company X has an opportunity to expand their presence. Within this paper, we compare many external research studies to better quantify the Total Addressable Market of the Edge Computing space. Furthermore, we highlight which Segments within Edge Computing have the most opportunities for growth, along with identify a specific market strategy that Company X could do to capture market share within the most opportunistic segment.

ContributorsHamkins, Sean (Co-author) / Raimondi, Ronnie (Co-author) / Gandolfi, Micheal (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Value Creation: Repositioning into Medical Office

Description

This thesis examines the value creation potential of renovating an existing commercial real estate asset to a medical office. It begins by examining commercial real estate and the medical sector at a high level. It then discusses the various criteria used to select a subject property for renovation. This renovation…

This thesis examines the value creation potential of renovating an existing commercial real estate asset to a medical office. It begins by examining commercial real estate and the medical sector at a high level. It then discusses the various criteria used to select a subject property for renovation. This renovation is then depicted through a modified pitch book that contains a financial model and pro forma.

ContributorsPeters, Matthew Scott (Co-author) / Larrea, Justin (Co-author) / Berger, Nicholas (Co-author) / Simonson, Mark (Thesis director) / Gray, William (Committee member) / Department of Finance (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Value Creation: Repositioning into Medical Office

Description

This thesis examines the value creation potential of renovating an existing commercial real estate asset to a medical office. It begins by examining commercial real estate and the medical sector at a high level. It then discusses the various criteria used to select a subject property for renovation. This renovation…

This thesis examines the value creation potential of renovating an existing commercial real estate asset to a medical office. It begins by examining commercial real estate and the medical sector at a high level. It then discusses the various criteria used to select a subject property for renovation. This renovation is then depicted through a modified pitch book that contains a financial model and pro forma.

ContributorsLarrea, Justin (Co-author) / Berger, Nicholas (Co-author) / Peters, Matthew (Co-author) / Simonson, Mark (Thesis director) / Gray, William (Committee member) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Company X Digital Education Thesis Project

Description

This paper goes does a market analysis on Inter Active Flat Panel Displays (IFPDs), and talks about how company X can grow its market share in IFPDs.

ContributorsKoroli, Eri (Co-author) / Phillips, Maya (Co-author) / Morales, Herwin (Co-author) / Hauck, Tanner (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Company X Collaborative Thesis: Real Estate

Description

The COVID-19 pandemic has and will continue to radically shift the workplace. An increasing percentage of the workforce desires flexible working options and, as such, firms are likely to require less office space going forward. Additionally, the economic downturn caused by the pandemic provides an opportunity for companies to secure…

The COVID-19 pandemic has and will continue to radically shift the workplace. An increasing percentage of the workforce desires flexible working options and, as such, firms are likely to require less office space going forward. Additionally, the economic downturn caused by the pandemic provides an opportunity for companies to secure favorable rent rates on new lease agreements. This project aims to evaluate and measure Company X’s potential cost savings from terminating current leases and downsizing office space in five selected cities. Along with city-specific real estate market research and forecasts, we employ a four-stage model of Company X’s real estate negotiation process to analyze whether existing lease agreements in these cities should be renewed or terminated.

ContributorsHegardt, Brandon Michael (Co-author) / Saker, Logan (Co-author) / Patterson, Jack (Co-author) / Ries, Sarah (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)

Created2021-05

Integrative analyses of diverse biological data sources

Description

The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards…

The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.

ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)

Created2011

System complexity reduction via feature selection

Description

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve…

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.

ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)

Created2011