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Colorado State University
1.
Vento, Noah Francis Ryoichi.
Hypothesis-based machine learning for deep-water channel systems.
Degree: MS(M.S.), Geosciences, 2020, Colorado State University
URL: http://hdl.handle.net/10217/208498 
► Machine learning algorithms are readily being incorporated into petroleum industry workflows for use in well-log correlation, prediction of rock properties, and seismic data interpretation. However,…
(more)
▼ Machine learning algorithms are readily being incorporated into petroleum industry workflows for use in well-log correlation, prediction of rock properties, and seismic data interpretation. However, there is a clear disconnect between sedimentology and data analytics in these workflows because sedimentologic data is largely qualitative and descriptive. Sedimentology defines stratigraphic architecture and heterogeneity, which can greatly impact reservoir quality and connectivity and thus hydrocarbon recovery. Deep-water channel systems are an example where predicting reservoir architecture is critical to mitigating risk in hydrocarbon exploration. Deep-water reservoirs are characterized by spatial and temporal variations in channel body stacking patterns, which are difficult to predict with the paucity of borehole data and low quality seismic available in these remote locations. These stacking patterns have been shown to be a key variable that controls reservoir connectivity. In this study, the gap between sedimentology and data analytics is bridged using machine learning algorithms to predict stratigraphic architecture and heterogeneity in a deep-water slope channel system. The algorithms classify variables that capture channel stacking patterns (i.e., channel positions: axis, off-axis, and margin) from a database of outcrop statistics sourced from 68 stratigraphic measured sections from outcrops of the Upper Cretaceous Tres Pasos Formation at Laguna Figueroa in the Magallanes Basin, Chile. An initial hypothesis that channel position could be predicted from 1D descriptive sedimentologic data was tested with a series of machine learning algorithms and classification schemes. The results confirmed this hypothesis as complex algorithms (i.e., random forest, XGBoost, and neural networks) achieved accuracies above 80% while less complex algorithms (i.e., decision trees) achieved lower accuracies between 60%-70%. However, certain classes were difficult for the machine learning algorithms to classify, such as the transitional off-axis class. Additionally, an interpretive classification scheme performed better (by around 10%-20% in some cases) than a geometric scheme that was devised to remove interpretation bias. However, outcrop observations reveal that the interpretive classification scheme may be an over-simplified approach and that more heterogeneity likely exists in each class as revealed by the geometric scheme. A refined hypothesis was developed that a hierarchical machine learning approach could lend deeper insight into the heterogeneity within sedimentologic classes that are difficult for an interpreter to discern by observation alone. This hierarchical analysis revealed distinct sub-classes in the margin channel position that highlight variations in margin depositional style. The conceptual impact of these varying margin styles on fluid flow and connectivity is shown.
Advisors/Committee Members: Stright, Lisa (advisor), Ronayne, Michael (committee member), Anderson, Charles (committee member).
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APA (6th Edition):
Vento, N. F. R. (2020). Hypothesis-based machine learning for deep-water channel systems. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/208498
Chicago Manual of Style (16th Edition):
Vento, Noah Francis Ryoichi. “Hypothesis-based machine learning for deep-water channel systems.” 2020. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/208498.
MLA Handbook (7th Edition):
Vento, Noah Francis Ryoichi. “Hypothesis-based machine learning for deep-water channel systems.” 2020. Web. 27 Feb 2021.
Vancouver:
Vento NFR. Hypothesis-based machine learning for deep-water channel systems. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/208498.
Council of Science Editors:
Vento NFR. Hypothesis-based machine learning for deep-water channel systems. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/208498
Colorado State University
2.
Bucher, Jake Duvall.
Case study of the real world integration of fuel cell plug-in hybrid electric vehicles and their effect on hydrogen refueling locations in the Puget Sound region.
Degree: MS(M.S.), Mechanical Engineering, 2014, Colorado State University
URL: http://hdl.handle.net/10217/83907
► The personal vehicle transportation fleet relies heavily on non-renewable and pollutive sources of fuel, such as petroleum. However, with harsher restrictions from the Environmental Protection…
(more)
▼ The personal vehicle transportation fleet relies heavily on non-renewable and pollutive sources of fuel, such as petroleum. However, with harsher restrictions from the Environmental Protection Agency's (EPA) Corporate Average Fuel Economy (CAFE) and California Air Resource Board's (CARB) Zero Emission Vehicle (ZEV) standards coupled with growing sales for alternative fueled vehicles, the automotive industry has begun to shift toward more renewable and clean sources of energy to power vehicles. The fuel cell plug-in hybrid electric vehicle (FCPHEV) architecture provides a unique and promising solution to decreasing the dependence of vehicles on petroleum and decreasing the amount of pollution emitted from tailpipes. Until recently, the FCPHEV architecture had only been developed in concept cars and paper studies. However, recent studies have confirmed the capability of the FCPHEV concept in terms of its economics, environmental benefits, and real-world viability. From this concept it becomes important to understand how daily commuters will benefit from driving a FCPHEV using real world driving data. Through the use of geographic information system (GIS) data of vehicle travel in the Puget Sound area from the National Renewable Energy Laboratory (NREL) a model of electrical and hydrogen energy consumption of a fleet of FCPHEVs can be constructed. This model can be modified to model the driving, charging and fueling habits of drivers using four different all-electric driving ranges, and using either a normal plug-in hybrid control strategy or a control strategy that focuses on highway fuel cell operation. These comparisons are used to analyze the driving habits of daily commuters while using a FCPHEV, and the effect of the FCPHEV architecture on the location of hydrogen refueling. The results of this thesis help to define FCPHEV energy management strategies and show that the FCPHEV architecture can concentrate the location of hydrogen refueling to predictable areas and aid in the development of the hydrogen refueling infrastructure.
Advisors/Committee Members: Bradley, Thomas (advisor), Anderson, Charles (committee member), Suryanarayanan, Siddarth (committee member).
Subjects/Keywords: energy consumption; utility factor; hydrogen; geographical information systems; fuel cell plug-in hybrid electric vehicle
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APA (6th Edition):
Bucher, J. D. (2014). Case study of the real world integration of fuel cell plug-in hybrid electric vehicles and their effect on hydrogen refueling locations in the Puget Sound region. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/83907
Chicago Manual of Style (16th Edition):
Bucher, Jake Duvall. “Case study of the real world integration of fuel cell plug-in hybrid electric vehicles and their effect on hydrogen refueling locations in the Puget Sound region.” 2014. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/83907.
MLA Handbook (7th Edition):
Bucher, Jake Duvall. “Case study of the real world integration of fuel cell plug-in hybrid electric vehicles and their effect on hydrogen refueling locations in the Puget Sound region.” 2014. Web. 27 Feb 2021.
Vancouver:
Bucher JD. Case study of the real world integration of fuel cell plug-in hybrid electric vehicles and their effect on hydrogen refueling locations in the Puget Sound region. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/83907.
Council of Science Editors:
Bucher JD. Case study of the real world integration of fuel cell plug-in hybrid electric vehicles and their effect on hydrogen refueling locations in the Puget Sound region. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/83907
Colorado State University
3.
Ugave, Viney Anand.
Smart indoor localization using machine learning techniques.
Degree: MS(M.S.), Electrical and Computer Engineering, 2014, Colorado State University
URL: http://hdl.handle.net/10217/84567
► The advancement of smartphone devices has led to a generation of new applications and solutions. These devices give away a great deal of information about…
(more)
▼ The advancement of smartphone devices has led to a generation of new applications and solutions. These devices give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user's context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization whereas interest in location-based services is also rising. While numerous smartphone based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. More importantly, these techniques entail high-energy consumption that can quickly drain a smartphone battery. In this thesis, we propose innovative techniques based on machine learning algorithms and smart sensor management for effective Indoor Localization using smartphones. We evaluated our techniques on several indoor environments with diverse characteristics and show improvements over several
state-of-the-art techniques from prior work. The extensive use of sensors and Wi-Fi scans can deplete the smartphone battery and so we quantitatively accounted for all the modules that consume the battery power. We also performed energy and accuracy tradeoff analysis to provide a broader understanding of how to smartly use these techniques. Furthermore, we investigated, implemented and tested both sensor and machine learning based techniques. Our best technique achieved an average accuracy between 1-3 meters across most of our evaluated indoor paths.
Advisors/Committee Members: Pasricha, Sudeep (advisor), Anderson, Charles (committee member), Roy, Sourajeet (committee member).
Subjects/Keywords: energy; indoor; localization; navigation; optimization; smartphones
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APA (6th Edition):
Ugave, V. A. (2014). Smart indoor localization using machine learning techniques. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/84567
Chicago Manual of Style (16th Edition):
Ugave, Viney Anand. “Smart indoor localization using machine learning techniques.” 2014. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/84567.
MLA Handbook (7th Edition):
Ugave, Viney Anand. “Smart indoor localization using machine learning techniques.” 2014. Web. 27 Feb 2021.
Vancouver:
Ugave VA. Smart indoor localization using machine learning techniques. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/84567.
Council of Science Editors:
Ugave VA. Smart indoor localization using machine learning techniques. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/84567
Colorado State University
4.
Sharma, Nand.
Single-trial P300 classification using PCA with LDA and neural networks.
Degree: MS(M.S.), Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/81079
► A brain-computer interface (BCI) is a device that uses brain signals to provide a non-muscular communication channel for motor-impaired patients. It is especially targeted at…
(more)
▼ A brain-computer interface (BCI) is a device that uses brain signals to provide a non-muscular communication channel for motor-impaired patients. It is especially targeted at patients with 'locked-in' syndrome, a condition where the patient is awake and fully aware but cannot communicate with the outside world due to complete paralysis. The P300 event-related potential (ERP), evoked in scalp-recorded electroencephalography (EEG) by external stimuli, has proven to be a reliable response for controlling a BCI. The P300 component of an event related potential is thus widely used in brain-computer interfaces to translate the subjects' intent by mere thoughts into commands to control artificial devices. The main challenge in the classification of P300 trials in electroencephalographic (EEG) data is the low signal-to-noise ratio (SNR) of the P300 response. To overcome the low SNR of individual trials, it is common practice to average together many consecutive trials, which effectively diminishes the random noise. Unfortunately, when more repeated trials are required for applications such as the P300 speller, the communication rate is greatly reduced. This has resulted in a need for better methods to improve single-trial classification accuracy of P300 response. In this work, we use Principal Component Analysis (PCA) as a preprocessing method and use Linear Discriminant Analysis (LDA)and neural networks for classification. The results show that a combination of PCA with these methods provided as high as 13% accuracy gain while using only 3 to 4 principal components. So, PCA feature selection not only increased the classification accuracy but also reduced the execution time of the algorithms by the resulting dimensionality reduction. It was also observed that when treating each data sample from each EEG channel as a separate data sample, PCA successfully separates out the variance across channels.
Advisors/Committee Members: Anderson, Charles (advisor), Kirby, Michael (advisor), Peterson, Chris (committee member).
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APA (6th Edition):
Sharma, N. (2013). Single-trial P300 classification using PCA with LDA and neural networks. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/81079
Chicago Manual of Style (16th Edition):
Sharma, Nand. “Single-trial P300 classification using PCA with LDA and neural networks.” 2013. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/81079.
MLA Handbook (7th Edition):
Sharma, Nand. “Single-trial P300 classification using PCA with LDA and neural networks.” 2013. Web. 27 Feb 2021.
Vancouver:
Sharma N. Single-trial P300 classification using PCA with LDA and neural networks. [Internet] [Masters thesis]. Colorado State University; 2013. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/81079.
Council of Science Editors:
Sharma N. Single-trial P300 classification using PCA with LDA and neural networks. [Masters Thesis]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/81079
Colorado State University
5.
Schwickerath, Anthony N.
Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm.
Degree: MS(M.S.), Mathematics, 2012, Colorado State University
URL: http://hdl.handle.net/10217/68152
► There is currently interest in detecting the use of chemical and biological weapons using hyperspectral sensors. Much of the research in this area assumes the…
(more)
▼ There is currently interest in detecting the use of chemical and biological weapons using hyperspectral sensors. Much of the research in this area assumes the spectral signature of the weapon is known in advance. Unfortunately, this may not always be the case. To obviate the reliance on a library of known target signatures, we instead view this as an anomaly detection problem. In this thesis, the RX algorithm, a benchmark anomaly detection algorithm for multi- and hyper-spectral data is reviewed, as are some standard extensions. This class of likelihood ratio test-based algorithms is generally applied to aerial imagery for the identification of man-made artifacts. As such, the model assumes that the scale is relatively consistent and that the targets (roads, cars) also have fixed sizes. We apply these methods to terrestrial video of biological and chemical aerosol plumes, where the background scale and target size both vary, and compare preliminary results. To explore the impact of parameter choice on algorithm performance, we also present an empirical study of the standard RX algorithm applied to synthetic targets of varying sizes over a range of settings.
Advisors/Committee Members: Kirby, Michael (advisor), Peterson, Christopher (committee member), Anderson, Charles (committee member).
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APA ·
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MLA ·
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APA (6th Edition):
Schwickerath, A. N. (2012). Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/68152
Chicago Manual of Style (16th Edition):
Schwickerath, Anthony N. “Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm.” 2012. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/68152.
MLA Handbook (7th Edition):
Schwickerath, Anthony N. “Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm.” 2012. Web. 27 Feb 2021.
Vancouver:
Schwickerath AN. Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/68152.
Council of Science Editors:
Schwickerath AN. Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/68152
Colorado State University
6.
Verlekar, Prathamesh.
Detecting error related negativity using EEG potentials generated during simulated brain computer interaction.
Degree: MS(M.S.), Computer Science, 2014, Colorado State University
URL: http://hdl.handle.net/10217/84568
► Error related negativity (ERN) is one of the components of the Event-Related Potential (ERP) observed during stimulus based tasks. In order to improve the performance…
(more)
▼ Error related negativity (ERN) is one of the components of the Event-Related Potential (ERP) observed during stimulus based tasks. In order to improve the performance of a brain computing interface (BCI) system, it is important to capture the ERN, classify the trials as correct or incorrect and feed this information back to the system. The objective of this study was to investigate techniques to detect presence of ERN in trials. In this thesis, features based on averaged ERP recordings were used to classify incorrect from correct actions. One feature selection technique coupled with four classification methods were used and compared in this work. Data were obtained from healthy subjects who performed an interaction experiment and the presence of ERN indicating incorrect responses was studied. Using suitable classifiers trained on data recorded earlier, the average recognition rate of correct and erroneous trials was reported and analyzed. The significance of selecting a subset of features to reduce the data dimensionality and to improve the classification performance was explored and discussed. We obtained success rates as high as 72% using a highly compact feature subset.
Advisors/Committee Members: Anderson, Charles (advisor), Ruiz, Jaime (committee member), Davies, Patricia (committee member).
Subjects/Keywords: RFE; SVM; brain computer interface; machine learning; neural network; EEG
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APA ·
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APA (6th Edition):
Verlekar, P. (2014). Detecting error related negativity using EEG potentials generated during simulated brain computer interaction. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/84568
Chicago Manual of Style (16th Edition):
Verlekar, Prathamesh. “Detecting error related negativity using EEG potentials generated during simulated brain computer interaction.” 2014. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/84568.
MLA Handbook (7th Edition):
Verlekar, Prathamesh. “Detecting error related negativity using EEG potentials generated during simulated brain computer interaction.” 2014. Web. 27 Feb 2021.
Vancouver:
Verlekar P. Detecting error related negativity using EEG potentials generated during simulated brain computer interaction. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/84568.
Council of Science Editors:
Verlekar P. Detecting error related negativity using EEG potentials generated during simulated brain computer interaction. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/84568
Colorado State University
7.
Kumar, Anurag.
Policy optimization for industrial benchmark using deep reinforcement learning.
Degree: MS(M.S.), Computer Science, 2020, Colorado State University
URL: http://hdl.handle.net/10217/212061
► Significant advancements have been made in the field of Reinforcement Learning (RL) in recent decades. Numerous novel RL environments and algorithms are mastering these problems…
(more)
▼ Significant advancements have been made in the field of Reinforcement Learning (RL) in recent decades. Numerous novel RL environments and algorithms are mastering these problems that have been studied, evaluated, and published. The most popular RL benchmark environments produced by OpenAI Gym and DeepMind Labs are modeled after single/multi-player board, video games, or single-purpose robots and the RL algorithms modeling optimal policies for playing those games have even outperformed humans in almost all of them. However, the real-world applications using RL is very limited, as the academic community has limited access to real industrial data and applications. Industrial Benchmark (IB) is a novel RL benchmark motivated by Industrial Control problems with properties such as continuous
state and action spaces, high dimensionality, partially observable
state space, delayed effects combined with complex heteroscedastic stochastic behavior. We have used Deep Reinforcement Learning (DRL) algorithms like Deep Q-Networks (DQN) and Double-DQN (DDQN) to study and model optimal policies on IB. Our empirical results show various DRL models outperforming previously published models on the same IB.
Advisors/Committee Members: Anderson, Charles (advisor), Chitsaz, Hamid (committee member), Kirby, Michael (committee member).
Subjects/Keywords: deep reinforcement learning; industrial benchmark; DDQN; q-learning; DQN
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APA (6th Edition):
Kumar, A. (2020). Policy optimization for industrial benchmark using deep reinforcement learning. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/212061
Chicago Manual of Style (16th Edition):
Kumar, Anurag. “Policy optimization for industrial benchmark using deep reinforcement learning.” 2020. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/212061.
MLA Handbook (7th Edition):
Kumar, Anurag. “Policy optimization for industrial benchmark using deep reinforcement learning.” 2020. Web. 27 Feb 2021.
Vancouver:
Kumar A. Policy optimization for industrial benchmark using deep reinforcement learning. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/212061.
Council of Science Editors:
Kumar A. Policy optimization for industrial benchmark using deep reinforcement learning. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/212061
Colorado State University
8.
Kapse, Ishan Deepak.
Novel methods to quantify aleatory and epistemic uncertainty in high speed networks.
Degree: MS(M.S.), Electrical and Computer Engineering, 2017, Colorado State University
URL: http://hdl.handle.net/10217/184055
► With the sustained miniaturization of integrated circuits to sub-45 nm regime and the increasing packaging density, random process variations have been found to result in…
(more)
▼ With the sustained miniaturization of integrated circuits to sub-45 nm regime and the increasing packaging density, random process variations have been found to result in unpredictability in circuit performance. In existing literature, this unpredictability has been modeled by creating polynomial expansions of random variables. But the existing methods prove inefficient because as the number of random variables within a system increase, the time and computational cost increases in a near-polynomial fashion. In order to mitigate this poor scalability of conventional approaches, several techniques are presented, in this dissertation, to sparsify the polynomial expansion. The sparser polynomial expansion is created, by identifying the contribution of each random variable on the total response of the system. This sparsification is performed primarily using two different methods. It translates to immense savings, in the time required, and the memory cost of computing the expansion. One of the two methods presented is applied to aleatory variability problems while the second method is applied to problems involving epistemic uncertainty. The accuracy of the proposed approaches is validated through multiple numerical examples.
Advisors/Committee Members: Roy, Sourajeet (advisor), Pasricha, Sudeep (committee member), Anderson, Charles (committee member).
Subjects/Keywords: epistemic uncertainty; uncertainty quantification; fuzzy sets; aleatory uncertainty
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APA ·
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APA (6th Edition):
Kapse, I. D. (2017). Novel methods to quantify aleatory and epistemic uncertainty in high speed networks. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/184055
Chicago Manual of Style (16th Edition):
Kapse, Ishan Deepak. “Novel methods to quantify aleatory and epistemic uncertainty in high speed networks.” 2017. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/184055.
MLA Handbook (7th Edition):
Kapse, Ishan Deepak. “Novel methods to quantify aleatory and epistemic uncertainty in high speed networks.” 2017. Web. 27 Feb 2021.
Vancouver:
Kapse ID. Novel methods to quantify aleatory and epistemic uncertainty in high speed networks. [Internet] [Masters thesis]. Colorado State University; 2017. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/184055.
Council of Science Editors:
Kapse ID. Novel methods to quantify aleatory and epistemic uncertainty in high speed networks. [Masters Thesis]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/184055
Colorado State University
9.
Mankovich, Nathan.
Methods for network generation and spectral feature selection: especially on gene expression data.
Degree: MS(M.S.), Mathematics, 2019, Colorado State University
URL: http://hdl.handle.net/10217/199775
► Feature selection is an essential step in many data analysis pipelines due to its ability to remove unimportant data. We will describe how to realize…
(more)
▼ Feature selection is an essential step in many data analysis pipelines due to its ability to remove unimportant data. We will describe how to realize a data set as a network using correlation, partial correlation, heat kernel and random edge generation methods. Then we lay out how to select features from these networks mainly leveraging the spectrum of the graph Laplacian, adjacency, and supra-adjacency matrices. We frame this work in the context of gene co-expression network analysis and proceed with a brief analysis of a small set of gene expression data for human subjects infected with the flu virus. We are able to distinguish two sets of 14-15 genes which produce two fold SSVM classification accuracies at certain times that are at least as high as classification accuracies done with more than 12,000 genes.
Advisors/Committee Members: Kirby, Michael (advisor), Anderson, Charles (committee member), Peterson, Chris (committee member).
Subjects/Keywords: feature selection; Laplacian; spectral; influenza; centrality; network
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APA (6th Edition):
Mankovich, N. (2019). Methods for network generation and spectral feature selection: especially on gene expression data. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/199775
Chicago Manual of Style (16th Edition):
Mankovich, Nathan. “Methods for network generation and spectral feature selection: especially on gene expression data.” 2019. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/199775.
MLA Handbook (7th Edition):
Mankovich, Nathan. “Methods for network generation and spectral feature selection: especially on gene expression data.” 2019. Web. 27 Feb 2021.
Vancouver:
Mankovich N. Methods for network generation and spectral feature selection: especially on gene expression data. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/199775.
Council of Science Editors:
Mankovich N. Methods for network generation and spectral feature selection: especially on gene expression data. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/199775
Colorado State University
10.
Pawlowski, Ben.
Modeling, simulation, and control of soft robots.
Degree: MS(M.S.), Mechanical Engineering, 2019, Colorado State University
URL: http://hdl.handle.net/10217/199791
► Soft robots are a new type of robot with deformable bodies and muscle-like actuations, which are fundamentally different from traditional robots with rigid links and…
(more)
▼ Soft robots are a new type of robot with deformable bodies and muscle-like actuations, which are fundamentally different from traditional robots with rigid links and motor-based actuators. Owing to their elasticity, soft robots outperform rigid ones in safety, maneuverability, and adaptability. With their advantages, many soft robots have been developed for manipulation and locomotion in recent years. However, the current
state of soft robotics has significant design and development work, but lags behind in modeling and control due to the complex dynamic behavior of the soft bodies. This complexity prevents a unified dynamics model that captures the dynamic behavior, computationally-efficient algorithms to simulate the dynamics in real-time, and closed-loop control algorithms to accomplish desired dynamic responses. In this thesis, we address the three problems of modeling, simulation, and control of soft robots. For the modeling, we establish a general modeling framework for the dynamics by integrating Cosserat theory with Hamilton's principle. Such a framework can accommodate different actuation methods (e.g., pneumatic, cable-driven, artificial muscles, etc.). To simulate the proposed models, we develop efficient numerical algorithms and implement them in C++ to simulate the dynamics of soft robots in real-time. These algorithms consider qualities of the dynamics that are typically neglected (e.g., numerical damping, group structure). Using the developed numerical algorithms, we investigate the control of soft robots with the goal of achieving real-time and closed-loop control policies. Several control approaches are tested (e.g., model predictive control, reinforcement learning) for a few key tasks: reaching various points in a soft manipulator's workspace and tracking a given trajectory. The results show that model predictive control is possible but is computationally demanding, while reinforcement learning techniques are more computationally effective but require a substantial number of training samples. The modeling, simulation, and control framework developed in this thesis will lay a solid foundation to unleash the potential of soft robots for various applications, such as manipulation and locomotion.
Advisors/Committee Members: Zhao, Jianguo (advisor), Puttlitz, Christian (committee member), Anderson, Charles (committee member).
Subjects/Keywords: reinforcement learning; symplectic integration; soft robots; model predictive control
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APA (6th Edition):
Pawlowski, B. (2019). Modeling, simulation, and control of soft robots. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/199791
Chicago Manual of Style (16th Edition):
Pawlowski, Ben. “Modeling, simulation, and control of soft robots.” 2019. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/199791.
MLA Handbook (7th Edition):
Pawlowski, Ben. “Modeling, simulation, and control of soft robots.” 2019. Web. 27 Feb 2021.
Vancouver:
Pawlowski B. Modeling, simulation, and control of soft robots. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/199791.
Council of Science Editors:
Pawlowski B. Modeling, simulation, and control of soft robots. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/199791
Colorado State University
11.
Trinko, David A.
Predictive energy management strategies for hybrid electric vehicles applied during acceleration events.
Degree: MS(M.S.), Mechanical Engineering, 2019, Colorado State University
URL: http://hdl.handle.net/10217/195328
► The emergence and widespread adoption of vehicles with hybrid powertrains and onboard computing capabilities have improved the feasibility of utilizing predictions of vehicle state to…
(more)
▼ The emergence and widespread adoption of vehicles with hybrid powertrains and onboard computing capabilities have improved the feasibility of utilizing predictions of vehicle
state to enable optimal energy management strategies (EMS) to improve fuel economy. Real-world implementation of optimal EMS remains challenging in part because of limits on prediction accuracy and computation speed. However, if a finite set of EMS can be pre-derived offline, instead of onboard the vehicle in real time, fuel economy improvements may be possible using hardware that is common in current production vehicles. Acceleration events (AE) are attractive targets for this kind of EMS application due to their high energy cost, probability of recurrence, and limited variability. This research aims to understand how a finite set of EMS might be derived and applied to AEs based on predictions of basic AE attributes to achieve reliable fuel economy improvements. Models of the 2010 Toyota Prius are used to simulate fuel economy for a variety of control strategies, including baseline control, optimal EMS control derived via dynamic programming, and pre-derived control applied with approximate prediction to AEs. Statistical methods are used to identify correlations between AE attributes, optimal powertrain control, and fuel economy results. Then, key AE attributes are used to define AE categorization schemes of various resolutions, in which one pre-derived EMS is applied to every AE in a category. Last, the control strategies are simulated during a variety of drive cycles to predict real-world fuel economy results. By simulating fuel economy improvement for AEs both in isolation and in the context of drive cycles, it was concluded that applying pre-derived EMS to AEs based on predictions of initial and final velocity is likely to enable reliable fuel economy benefits in low-aggression driving.
Advisors/Committee Members: Bradley, Thomas H. (advisor), Quinn, Jason C. (committee member), Anderson, Charles W. (committee member).
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APA (6th Edition):
Trinko, D. A. (2019). Predictive energy management strategies for hybrid electric vehicles applied during acceleration events. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/195328
Chicago Manual of Style (16th Edition):
Trinko, David A. “Predictive energy management strategies for hybrid electric vehicles applied during acceleration events.” 2019. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/195328.
MLA Handbook (7th Edition):
Trinko, David A. “Predictive energy management strategies for hybrid electric vehicles applied during acceleration events.” 2019. Web. 27 Feb 2021.
Vancouver:
Trinko DA. Predictive energy management strategies for hybrid electric vehicles applied during acceleration events. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/195328.
Council of Science Editors:
Trinko DA. Predictive energy management strategies for hybrid electric vehicles applied during acceleration events. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/195328
Colorado State University
12.
Edwards, Jacob.
Diverse developmental trajectories of perineuronal nets during vertebrate nervous system construction.
Degree: MS(M.S.), Biology, 2018, Colorado State University
URL: http://hdl.handle.net/10217/189416
► In the central nervous system, aggregated extracellular matrix compounds known as perineuronal nets (PNNs) shape patterns of neural connectivity over development. Removing PNNs restores juvenile-like…
(more)
▼ In the central nervous system, aggregated extracellular matrix compounds known as perineuronal nets (PNNs) shape patterns of neural connectivity over development. Removing PNNs restores juvenile-like states of neural circuit plasticity and subsequent behavioral plasticity. Our current understanding of the role of PNNs in plasticity has resulted in promising therapeutic applications for many neurodegenerative diseases. To ensure safety and efficacy in such applications, we require a broad understanding of PNN function in the nervous system. The current data suggest that PNNs stabilize fundamental features of neural connectivity progressively in an ascending, or "ground-up", fashion. Stabilizing lower input processing pathways establishes a solid, reliable foundation for higher cognition. However, data on PNN development exists almost exclusively for mammals. Is, then, the ground-up model of circuit stabilization a general feature of PNNs across vertebrates? I found that developmental patterns of PNNs in fish (Poecilia reticulata), amphibians (Rhinella yunga), and reptiles (Anolis sagrei) follow diverse trajectories, often emerging first in higher forebrain processing pathways. Similarly, they associate with diverse cell populations and vary widely in structural characteristics both within and across species. While my data do not invalidate a ground-up model for mammal PNNs, they do suggest that this pattern may be an evolutionary innovation in this group, and that the broad roles of PNNs in circuit stability and neuronal physiology are complex and lineage-specific.
Advisors/Committee Members: Hoke, Kim (advisor), Anderson, Charles (committee member), Garrity, Deborah (committee member), Mueller, Rachel (committee member).
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APA (6th Edition):
Edwards, J. (2018). Diverse developmental trajectories of perineuronal nets during vertebrate nervous system construction. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/189416
Chicago Manual of Style (16th Edition):
Edwards, Jacob. “Diverse developmental trajectories of perineuronal nets during vertebrate nervous system construction.” 2018. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/189416.
MLA Handbook (7th Edition):
Edwards, Jacob. “Diverse developmental trajectories of perineuronal nets during vertebrate nervous system construction.” 2018. Web. 27 Feb 2021.
Vancouver:
Edwards J. Diverse developmental trajectories of perineuronal nets during vertebrate nervous system construction. [Internet] [Masters thesis]. Colorado State University; 2018. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/189416.
Council of Science Editors:
Edwards J. Diverse developmental trajectories of perineuronal nets during vertebrate nervous system construction. [Masters Thesis]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/189416
Colorado State University
13.
Blitch, John G.
Implications for automation assistance in unmanned aerial system operator training.
Degree: MS(M.S.), Psychology, 2012, Colorado State University
URL: http://hdl.handle.net/10217/67997
► The integration of automated modules into unmanned systems control has had a positive impact on operational effectiveness across a variety of challenging domains from battlefields…
(more)
▼ The integration of automated modules into unmanned systems control has had a positive impact on operational effectiveness across a variety of challenging domains from battlefields and disaster areas to the National Airspace and distant planets. Despite the generally positive nature of such technological progress, however, concerns for complacency and other automation-induced detriments have been established in a growing body of empirical literature derived from both laboratory research and operational reviews. Given the military's demand for new Unmanned Aerial System (UAS) operators, there is a need to explore how such concerns might extend from the operational realm of experienced professionals into the novice training environment. An experiment was conducted to investigate the influence of automation on training efficiency using a Predator UAS simulator developed by the Air Force Research Laboratory (AFRL) in a modified replication of previous research. Participants were trained in a series of basic maneuvers, with half receiving automated support only on a subset of maneuvers. A subsequent novel landing test showed poorer performance for the group that received assistance from automation during training. Implications of these findings are discussed.
Advisors/Committee Members: Clegg, Benjamin A. (advisor), Cleary, Anne (committee member), Anderson, Charles (committee member).
Subjects/Keywords: automation; cognitive workload; control; robotics; training; unmanned systems
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APA (6th Edition):
Blitch, J. G. (2012). Implications for automation assistance in unmanned aerial system operator training. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/67997
Chicago Manual of Style (16th Edition):
Blitch, John G. “Implications for automation assistance in unmanned aerial system operator training.” 2012. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/67997.
MLA Handbook (7th Edition):
Blitch, John G. “Implications for automation assistance in unmanned aerial system operator training.” 2012. Web. 27 Feb 2021.
Vancouver:
Blitch JG. Implications for automation assistance in unmanned aerial system operator training. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/67997.
Council of Science Editors:
Blitch JG. Implications for automation assistance in unmanned aerial system operator training. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/67997
Colorado State University
14.
Peterson, Erik J.
Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex.
Degree: MS(M.S.), Psychology, 2012, Colorado State University
URL: http://hdl.handle.net/10217/65351
► Lesion, drug, single-cell recording, as well as human fMRI studies, suggest dopaminergic projections from VTA/SNc (ventral tagmental area/substantia nigra pars compacta) and cortically driven striatal…
(more)
▼ Lesion, drug, single-cell recording, as well as human fMRI studies, suggest dopaminergic projections from VTA/SNc (ventral tagmental area/substantia nigra pars compacta) and cortically driven striatal activity plays a key role in associating sensory events with rewarding actions both by facilitating reward processing and prediction (i.e. reinforcement learning) and biasing and later updating action selection. We, for the first time, isolated BOLD signal changes for stimulus, pre-response, response and feedback delivery at three reward levels. This design allowed us to estimate the degree of involvement of individual striatal regions across these trial components, the reward sensitivity of each component and allowed for a novel comparison of potential (and potentially competing) reinforcement learning computations. Striatal and lateral premotor cortex regions of interest (ROIs) significant activations were universally observed (excepting the ventral striatum) during stimulus presentation, pre-response, response and feedback delivery, confirming these areas importance in all aspects of visuomotor learning. The head of the caudate showed a precipitous drop in activity pre-response, while in the body of the caudate showed no significant changes in activity. The putamen peaked in activity during response. Activation in the lateral premotor cortex was strongest during stimulus presentation, but the drop off was followed by a trend of increasing activity as feedback approached. Both the head and body of the caudate as well as the putamen displayed reward-level sensitivity only during stimulus, while the ventral striatum showed reward sensitivity at both stimulus and feedback. The lack of reward sensitivity surrounding response is inconsistent with theories that the head and ventral striatum encode the value of actions. Which of the three examined reinforcement learning models correlated best with BOLD signal changes varied as a function of trial component and ROI suggesting these regions computations vary depending on task demand.
Advisors/Committee Members: Seger, Carol A. (advisor), Troup, Lucy J. (committee member), Anderson, Charles W. (committee member).
Subjects/Keywords: basal ganglia; striatum; reward; response; dopamine
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APA (6th Edition):
Peterson, E. J. (2012). Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/65351
Chicago Manual of Style (16th Edition):
Peterson, Erik J. “Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex.” 2012. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/65351.
MLA Handbook (7th Edition):
Peterson, Erik J. “Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex.” 2012. Web. 27 Feb 2021.
Vancouver:
Peterson EJ. Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/65351.
Council of Science Editors:
Peterson EJ. Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/65351
Colorado State University
15.
Rowland, Christopher A.
Testing effects in context memory.
Degree: MS(M.S.), Psychology, 2011, Colorado State University
URL: http://hdl.handle.net/10217/46908
► Retrieving a previously learned piece of information can have profound positive effects on the later retention of such information. However, it is not clear if…
(more)
▼ Retrieving a previously learned piece of information can have profound positive effects on the later retention of such information. However, it is not clear if test-induced memory benefits are restricted to the specific information which was retrieved, or if they can generalize more completely to the full study episode. Two experiments investigated the role of retrieval practice on memory for both target and non-target contextual information. Experiment 1 used a remember-know task to assess the subjective quality of memory as a function of earlier retrieval practice or study. Additionally, memory for context information (target font color) from the initial study episode was assessed. Experiment 2 used paired associates to investigate the effect of testing on non-tested but associated contextual information. Successful retrieval practice, compared with study, resulted in large benefits in target, target-associated, and context information retention across both experiments. Moreover, successful retrieval practice was associated with a greater contribution of remember responses informing recognition decisions. The results suggest that retrieving information may serve to both boost item memory about a target and strengthen the bind between target and associated contextual information. In sum, the present study adds to an emerging literature that test-induced mnemonic benefits may "spill over" to non-tested information.
Advisors/Committee Members: DeLosh, Edward L. (advisor), Rhodes, Matthew G. (committee member), Anderson, Charles (committee member).
Subjects/Keywords: context; memory; retrieval; source memory; testing; testing effect
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APA (6th Edition):
Rowland, C. A. (2011). Testing effects in context memory. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/46908
Chicago Manual of Style (16th Edition):
Rowland, Christopher A. “Testing effects in context memory.” 2011. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/46908.
MLA Handbook (7th Edition):
Rowland, Christopher A. “Testing effects in context memory.” 2011. Web. 27 Feb 2021.
Vancouver:
Rowland CA. Testing effects in context memory. [Internet] [Masters thesis]. Colorado State University; 2011. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/46908.
Council of Science Editors:
Rowland CA. Testing effects in context memory. [Masters Thesis]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/46908
Colorado State University
16.
Ericson, Kathleen.
Robust health stream processing.
Degree: PhD, Computer Science, 2014, Colorado State University
URL: http://hdl.handle.net/10217/88427
► As the cost of personal health sensors decrease along with improvements in battery life and connectivity, it becomes more feasible to allow patients to leave…
(more)
▼ As the cost of personal health sensors decrease along with improvements in battery life and connectivity, it becomes more feasible to allow patients to leave full-time care environments sooner. Such devices could lead to greater independence for the elderly, as well as for others who would normally require full-time care. It would also allow surgery patients to spend less time in the hospital, both pre- and post-operation, as all data could be gathered via remote sensors in the patients home. While sensor technology is rapidly approaching the point where this is a feasible option, we still lack in processing frameworks which would make such a leap not only feasible but safe. This work focuses on developing a framework which is robust to both failures of processing elements as well as interference from other computations processing health sensor data. We work with 3 disparate data streams and accompanying computations: electroencephalogram (EEG) data gathered for a brain-computer interface (BCI) application, electrocardiogram (ECG) data gathered for arrhythmia detection, and thorax data gathered from monitoring patient sleep status.
Advisors/Committee Members: Pallickara, Shrideep (advisor), Massey, Daniel (committee member), Turk, Daniel (committee member), Anderson, Charles (committee member).
Subjects/Keywords: interference detection; health stream processing; stream processing; distributed systems; fault-tolerance
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APA (6th Edition):
Ericson, K. (2014). Robust health stream processing. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/88427
Chicago Manual of Style (16th Edition):
Ericson, Kathleen. “Robust health stream processing.” 2014. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/88427.
MLA Handbook (7th Edition):
Ericson, Kathleen. “Robust health stream processing.” 2014. Web. 27 Feb 2021.
Vancouver:
Ericson K. Robust health stream processing. [Internet] [Doctoral dissertation]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/88427.
Council of Science Editors:
Ericson K. Robust health stream processing. [Doctoral Dissertation]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/88427
Colorado State University
17.
Minhas, Fayyaz ul Amir Afsar.
Large margin methods for partner specific prediction of interfaces in protein complexes.
Degree: PhD, Computer Science, 2014, Colorado State University
URL: http://hdl.handle.net/10217/82500
► The study of protein interfaces and binding sites is a very important domain of research in bioinformatics. Information about the interfaces between proteins can be…
(more)
▼ The study of protein interfaces and binding sites is a very important domain of research in bioinformatics. Information about the interfaces between proteins can be used not only in understanding protein function but can also be directly employed in drug design and protein engineering. However, the experimental determination of protein interfaces is cumbersome, expensive and not possible in some cases with today's technology. As a consequence, the computational prediction of protein interfaces from sequence and structure has emerged as a very active research area. A number of machine learning based techniques have been proposed for the solution to this problem. However, the prediction accuracy of most such schemes is very low. In this dissertation we present large-margin classification approaches that have been designed to directly model different aspects of protein complex formation as well as the characteristics of available data. Most existing machine learning techniques for this task are partner-independent in nature, i.e., they ignore the fact that the binding propensity of a protein to bind to another protein is dependent upon characteristics of residues in both proteins. We have developed a pairwise support vector machine classifier called PAIRpred to predict protein interfaces in a partner-specific fashion. Due to its more detailed model of the problem, PAIRpred offers
state of the art accuracy in predicting both binding sites at the protein level as well as inter-protein residue contacts at the complex level. PAIRpred uses sequence and structure conservation, local structural similarity and surface geometry, residue solvent exposure and template based features derived from the unbound structures of proteins forming a protein complex. We have investigated the impact of explicitly modeling the inter-dependencies between residues that are imposed by the overall structure of a protein during the formation of a protein complex through transductive and semi-supervised learning models. We also present a novel multiple instance learning scheme called MI-1 that explicitly models imprecision in sequence-level annotations of binding sites in proteins that bind calmodulin to achieve
state of the art prediction accuracy for this task.
Advisors/Committee Members: Ben-Hur, Asa (advisor), Draper, Bruce (committee member), Anderson, Charles (committee member), Snow, Christopher (committee member).
Subjects/Keywords: bioinformatics; large margin methods; machine learning; protein interactions; protein interface prediction; proteins
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APA (6th Edition):
Minhas, F. u. A. A. (2014). Large margin methods for partner specific prediction of interfaces in protein complexes. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/82500
Chicago Manual of Style (16th Edition):
Minhas, Fayyaz ul Amir Afsar. “Large margin methods for partner specific prediction of interfaces in protein complexes.” 2014. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/82500.
MLA Handbook (7th Edition):
Minhas, Fayyaz ul Amir Afsar. “Large margin methods for partner specific prediction of interfaces in protein complexes.” 2014. Web. 27 Feb 2021.
Vancouver:
Minhas FuAA. Large margin methods for partner specific prediction of interfaces in protein complexes. [Internet] [Doctoral dissertation]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/82500.
Council of Science Editors:
Minhas FuAA. Large margin methods for partner specific prediction of interfaces in protein complexes. [Doctoral Dissertation]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/82500
Colorado State University
18.
O'Hara, Stephen.
Scalable learning of actions from unlabeled videos.
Degree: PhD, Computer Science, 2013, Colorado State University
URL: http://hdl.handle.net/10217/78864
► Emerging applications in human-computer interfaces, security, and robotics have a need for understanding human behavior from video data. Much of the research in the field…
(more)
▼ Emerging applications in human-computer interfaces, security, and robotics have a need for understanding human behavior from video data. Much of the research in the field of action recognition evaluates methods using relatively small data sets, under controlled conditions, and with a small set of allowable action labels. There are significant challenges in trying to adapt existing action recognition models to less structured and larger-scale data sets. Those challenges include: the recognition of a large vocabulary of actions, the scalability to learn from a large corpus of video data, the need for real-time recognition on streaming video, and the requirement to operate in settings with uncontrolled lighting, a variety of camera angles, dynamic backgrounds, and multiple actors. This thesis focuses on scalable methods for classifying and clustering actions with minimal human supervision. Unsupervised methods are emphasized in order to learn from a massive amount of unlabeled data, and for the potential to retrain models with minimal human intervention when adapting to new settings or applications. Because many applications of action recognition require real-time performance, and training data sets can be large, scalable methods for both learning and detection are beneficial. The specific contributions from this dissertation include a novel method for Approximate Nearest Neighbor (ANN) indexing of general metric spaces and the application of this structure to a manifold-based action representation. With this structure, nearest-neighbor action recognition is demonstrated to be comparable or superior to existing methods, while also being fast and scalable. Leveraging the same metric space indexing mechanism, a novel clustering method is introduced for discovering action exemplars in data.
Advisors/Committee Members: Draper, Bruce A. (advisor), Howe, Adele (committee member), Anderson, Charles (committee member), Peterson, Christopher (committee member).
Subjects/Keywords: action recognition; approximate nearest neighbor; Grassmann manifold; randomized forests; unsupervised learning; video analysis
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APA (6th Edition):
O'Hara, S. (2013). Scalable learning of actions from unlabeled videos. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/78864
Chicago Manual of Style (16th Edition):
O'Hara, Stephen. “Scalable learning of actions from unlabeled videos.” 2013. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/78864.
MLA Handbook (7th Edition):
O'Hara, Stephen. “Scalable learning of actions from unlabeled videos.” 2013. Web. 27 Feb 2021.
Vancouver:
O'Hara S. Scalable learning of actions from unlabeled videos. [Internet] [Doctoral dissertation]. Colorado State University; 2013. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/78864.
Council of Science Editors:
O'Hara S. Scalable learning of actions from unlabeled videos. [Doctoral Dissertation]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/78864
Colorado State University
19.
Sui, Zhiquan.
Distributed algorithms for the orchestration of stochastic discrete event simulations.
Degree: PhD, Computer Science, 2014, Colorado State University
URL: http://hdl.handle.net/10217/88541
► Discrete event simulations are widely used in modeling real-world phenomena such as epidemiology, congestion analysis, weather forecasting, economic activity, and chemical reactions. The expressiveness of…
(more)
▼ Discrete event simulations are widely used in modeling real-world phenomena such as epidemiology, congestion analysis, weather forecasting, economic activity, and chemical reactions. The expressiveness of such simulations depends on the number and types of entities that are modeled and also the interactions that entities have with each other. In the case of stochastic simulations, these interactions are based on the concomitant probability density functions. The more exhaustively a phenomena is modeled, the greater its computational complexity and, correspondingly, the execution time. Distributed orchestration can speed-up such complex simulations. This dissertation considers the problem of distributed orchestration of stochastic discrete event simulations where the computations are irregular and the processing loads stochastic. We have designed a suite of algorithms that target alleviating imbalances between processing elements across synchronization time steps. The algorithms explore different aspects of the orchestration spectrum: static vs. dynamic, reactive vs. proactive, and deterministic vs. learning-based. The feature vector that guides our algorithms include externally observable features of the simulation such as computational footprints and hardware profiles, and features internal to the simulation such as entity states. The learning structure includes basic version of Artificial Neural Network (ANN) and an improved version of ANN. The algorithms are self-tuning and account for the
state of the simulation and processing elements while coping with prediction errors. Finally, these algorithms address resource uncertainty as well. Resource uncertainty in such settings occurs due to resource failures, slowdowns, and heterogeneity. Task apportioning, speculative tasks to cope with stragglers, and checkpointing account for the quality and
state of both the resource and simulation. The algorithms achieve demonstrably good performance. Despite the irregular nature of these computations, stochasticity in the processing loads, and resource uncertainty execution times are reduced by a factor of 1.8 when the number of resources is doubled.
Advisors/Committee Members: Pallickara, Shrideep (advisor), Anderson, Charles (committee member), Böhm, Wim (committee member), Hayne, Stephen (committee member).
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APA (6th Edition):
Sui, Z. (2014). Distributed algorithms for the orchestration of stochastic discrete event simulations. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/88541
Chicago Manual of Style (16th Edition):
Sui, Zhiquan. “Distributed algorithms for the orchestration of stochastic discrete event simulations.” 2014. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/88541.
MLA Handbook (7th Edition):
Sui, Zhiquan. “Distributed algorithms for the orchestration of stochastic discrete event simulations.” 2014. Web. 27 Feb 2021.
Vancouver:
Sui Z. Distributed algorithms for the orchestration of stochastic discrete event simulations. [Internet] [Doctoral dissertation]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/88541.
Council of Science Editors:
Sui Z. Distributed algorithms for the orchestration of stochastic discrete event simulations. [Doctoral Dissertation]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/88541
Colorado State University
20.
Donohoo, Brad Kyoshi.
Machine learning techniques for energy optimization in mobile embedded systems.
Degree: MS(M.S.), Electrical and Computer Engineering, 2012, Colorado State University
URL: http://hdl.handle.net/10217/68004
► Mobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for…
(more)
▼ Mobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for communication, business, and social interactions. While performance, capabilities, and design are all important considerations when purchasing a mobile device, a long battery lifetime is one of the most desirable attributes. Battery technology and capacity has improved over the years, but it still cannot keep pace with the power consumption demands of today's mobile devices. This key limiter has led to a strong research emphasis on extending battery lifetime by minimizing energy consumption, primarily using software optimizations. This thesis presents two strategies that attempt to optimize mobile device energy consumption with negligible impact on user perception and quality of service (QoS). The first strategy proposes an application and user interaction aware middleware framework that takes advantage of user idle time between interaction events of the foreground application to optimize CPU and screen backlight energy consumption. The framework dynamically classifies mobile device applications based on their received interaction patterns, then invokes a number of different power management algorithms to adjust processor frequency and screen backlight levels accordingly. The second strategy proposes the usage of machine learning techniques to learn a user's mobile device usage pattern pertaining to spatiotemporal and device contexts, and then predict energy-optimal data and location interface configurations. By learning where and when a mobile device user uses certain power-hungry interfaces (3G, WiFi, and GPS), the techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression, and k-nearest neighbor, are able to dynamically turn off unnecessary interfaces at runtime in order to save energy.
Advisors/Committee Members: Pasricha, Sudeep (advisor), Anderson, Charles (committee member), Jayasumana, Anura P. (committee member).
Subjects/Keywords: energy optimization; smartphones; machine learning; human factors
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APA (6th Edition):
Donohoo, B. K. (2012). Machine learning techniques for energy optimization in mobile embedded systems. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/68004
Chicago Manual of Style (16th Edition):
Donohoo, Brad Kyoshi. “Machine learning techniques for energy optimization in mobile embedded systems.” 2012. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/68004.
MLA Handbook (7th Edition):
Donohoo, Brad Kyoshi. “Machine learning techniques for energy optimization in mobile embedded systems.” 2012. Web. 27 Feb 2021.
Vancouver:
Donohoo BK. Machine learning techniques for energy optimization in mobile embedded systems. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/68004.
Council of Science Editors:
Donohoo BK. Machine learning techniques for energy optimization in mobile embedded systems. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/68004
Colorado State University
21.
Dantanarayana, Navini.
Generative topographic mapping of electroencephalography (EEG) data.
Degree: MS(M.S.), Computer Science, 2014, Colorado State University
URL: http://hdl.handle.net/10217/88516
► Generative Topographic Mapping (GTM) assumes that the features of high dimensional data can be described by a few variables (usually 1 or 2). Based on…
(more)
▼ Generative Topographic Mapping (GTM) assumes that the features of high dimensional data can be described by a few variables (usually 1 or 2). Based on this assumption, the GTM trains unsupervised on the high dimensional data to find these variables from which the features can be generated. The variables can be used to represent and visualize the original data on a low dimensional space. Here, we have applied the GTM algorithm on Electroencephalography (EEG) signals in order to find a two dimensional representation for them. The 2-D representation can also be used to classify the EEG signals with P300 waves, an Event Related Potential (ERP) that occurs when the subject identifies a rare but expected stimulus. Furthermore, unsupervised feature learning capability of the GTM algorithm is investigated by providing EEG signals of different subjects and protocols. The results indicate that the algorithm successfully captures the feature variations in the data when generating the 2-D representation, therefore can be efficiently used as a powerful data visualization and analysis tool.
Advisors/Committee Members: Anderson, Charles (advisor), Ben-Hur, Asa (committee member), Davies, Patricia (committee member).
Subjects/Keywords: dimensionality reduction; generative topographic mapping; electroencephalography
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APA (6th Edition):
Dantanarayana, N. (2014). Generative topographic mapping of electroencephalography (EEG) data. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/88516
Chicago Manual of Style (16th Edition):
Dantanarayana, Navini. “Generative topographic mapping of electroencephalography (EEG) data.” 2014. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/88516.
MLA Handbook (7th Edition):
Dantanarayana, Navini. “Generative topographic mapping of electroencephalography (EEG) data.” 2014. Web. 27 Feb 2021.
Vancouver:
Dantanarayana N. Generative topographic mapping of electroencephalography (EEG) data. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/88516.
Council of Science Editors:
Dantanarayana N. Generative topographic mapping of electroencephalography (EEG) data. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/88516
Colorado State University
22.
McNeely-White, David G.
Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing.
Degree: MS(M.S.), Computer Science, 2020, Colorado State University
URL: http://hdl.handle.net/10217/208467
► Deep convolutional neural networks (CNNs) are the dominant technology in computer vision today. Much of the recent computer vision literature can be thought of as…
(more)
▼ Deep convolutional neural networks (CNNs) are the dominant technology in computer vision today. Much of the recent computer vision literature can be thought of as a competition to find the best architecture for vision within the deep convolutional framework. Despite all the effort invested in developing sophisticated convolutional architectures, however, it's not clear how different from each other the best CNNs really are. This thesis measures the similarity between ten well-known CNNs, in terms of the properties they extract from images. I find that the properties extracted by each of the ten networks are very similar to each other, in the sense that any of their features can be well approximated by an affine transformation of the features of any of the other nine. In particular, there is evidence that each network extracts mostly the same information as each other network, though some do it more robustly. The similarity between each of these CNNs is surprising. Convolutional neural networks learn complex non-linear features of images, and the architectural differences between systems suggest that these non-linear functions should take different forms. Nonetheless, these ten CNNs which were trained on the same data set seem to have learned to extract similar properties from images. In essence, each CNN's training algorithm hill-climbs in a very different parameter space, yet converges on a similar solution. This suggests that for CNNs, the selection of the training set and strategy may be more important than the selection of the convolutional architecture.
Advisors/Committee Members: Beveridge, J. Ross (advisor), Anderson, Charles W. (committee member), Seger, Carol A. (committee member).
Subjects/Keywords: convolutional neural networks; feature space; machine learning; feature mapping; computer vision; ImageNet
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APA (6th Edition):
McNeely-White, D. G. (2020). Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/208467
Chicago Manual of Style (16th Edition):
McNeely-White, David G. “Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing.” 2020. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/208467.
MLA Handbook (7th Edition):
McNeely-White, David G. “Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing.” 2020. Web. 27 Feb 2021.
Vancouver:
McNeely-White DG. Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/208467.
Council of Science Editors:
McNeely-White DG. Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/208467
Colorado State University
23.
Cashero, Zachary.
Comparison of EEG preprocessing methods to improve the performance of the P300 speller.
Degree: MS(M.S.), Computer Science, 2011, Colorado State University
URL: http://hdl.handle.net/10217/49866
► The classification of P300 trials in electroencephalographic (EEG) data is made difficult due the low signal-to-noise ratio (SNR) of the P300 response. To overcome the…
(more)
▼ The classification of P300 trials in electroencephalographic (EEG) data is made difficult due the low signal-to-noise ratio (SNR) of the P300 response. To overcome the low SNR of individual trials, it is common practice to average together many consecutive trials, which effectively diminishes the random noise. Unfortunately, when more repeated trials are required for applications such as the P300 speller, the communication rate is greatly reduced. Since the noise results from background brain activity and is inherent to the EEG recording methods, signal analysis techniques like blind source separation (BSS) have the potential to isolate the true source signal from the noise when using multi-channel recordings. This thesis provides a comparison of three BSS algorithms: independent component analysis (ICA), maximum noise fraction (MNF), and principal component analysis (PCA). In addition to this, the effects of adding temporal information to the original data, thereby creating time-delay embedded data, will be analyzed. The BSS methods can utilize this time-delay embedded data to find more complex spatio-temporal filters rather than the purely spatial filters found using the original data. One problem that is intrinsically tied to the application of BSS methods is the selection of the most relevant source components that are returned from each BSS algorithm. In this work, the following feature selection algorithms are adapted to be used for component selection: forward selection, ANOVA-based ranking, Relief, and recursive feature elimination (RFE). The performance metric used for all comparisons is the classification accuracy of P300 trials using a support vector machine (SVM) with a Gaussian kernel. The results show that although both BSS and feature selection algorithms can each cause significant performance gains, there is no added benefit from using both together. Feature selection is most beneficial when applied to a large number of electrodes, and BSS is most beneficial when applied to a smaller set of electrodes. Also, the results show that time-delay embedding is not beneficial for P300 classification.
Advisors/Committee Members: Anderson, Charles (advisor), Chen, Thomas (advisor), Tobet, Stuart (committee member), Ben-Hur, Asa (committee member).
Subjects/Keywords: blind source separation; brain computer interface; classification; P300 speller; signal analysis; EEG
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APA (6th Edition):
Cashero, Z. (2011). Comparison of EEG preprocessing methods to improve the performance of the P300 speller. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/49866
Chicago Manual of Style (16th Edition):
Cashero, Zachary. “Comparison of EEG preprocessing methods to improve the performance of the P300 speller.” 2011. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/49866.
MLA Handbook (7th Edition):
Cashero, Zachary. “Comparison of EEG preprocessing methods to improve the performance of the P300 speller.” 2011. Web. 27 Feb 2021.
Vancouver:
Cashero Z. Comparison of EEG preprocessing methods to improve the performance of the P300 speller. [Internet] [Masters thesis]. Colorado State University; 2011. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/49866.
Council of Science Editors:
Cashero Z. Comparison of EEG preprocessing methods to improve the performance of the P300 speller. [Masters Thesis]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/49866
Colorado State University
24.
Yaremenko, Vladimir.
Unattended acoustic sensor systems for noise monitoring in national parks.
Degree: MS(M.S.), Electrical and Computer Engineering, 2017, Colorado State University
URL: http://hdl.handle.net/10217/181332
► Detection and classification of transient acoustic signals is a difficult problem. The problem is often complicated by factors such as the variety of sources that…
(more)
▼ Detection and classification of transient acoustic signals is a difficult problem. The problem is often complicated by factors such as the variety of sources that may be encountered, the presence of strong interference and substantial variations in the acoustic environment. Furthermore, for most applications of transient detection and classification, such as speech recognition and environmental monitoring, online detection and classification of these transient events is required. This is even more crucial for applications such as environmental monitoring as it is often done at remote locations where it is unfeasible to set up a large, general-purpose processing system. Instead, some type of custom-designed system is needed which is power efficient yet able to run the necessary signal processing algorithms in near real-time. In this thesis, we describe a custom-designed environmental monitoring system (EMS) which was specifically designed for monitoring air traffic and other sources of interest in national parks. More specifically, this thesis focuses on the capabilities of the EMS and how transient detection, classification and tracking are implemented on it. The Sparse Coefficient
State Tracking (SCST) transient detection and classification algorithm was implemented on the EMS board in order to detect and classify transient events. This algorithm was chosen because it was designed for this particular application and was shown to have superior performance compared to other algorithms commonly used for transient detection and classification. The SCST algorithm was implemented on an Artix 7 FPGA with parts of the algorithm running as dedicated custom logic and other parts running sequentially on a soft-core processor. In this thesis, the partitioning and pipelining of this algorithm is explained. Each of the partitions was tested independently to very their functionality with respect to the overall system. Furthermore, the entire SCST algorithm was tested in the field on actual acoustic data and the performance of this implementation was evaluated using receiver operator characteristic (ROC) curves and confusion matrices. In this test the FPGA implementation of SCST was able to achieve acceptable source detection and classification results despite a difficult data set and limited training data. The tracking of acoustic sources is done through successive direction of arrival (DOA) angle estimation using a wideband extension of the Capon beamforming algorithm. This algorithm was also implemented on the EMS in order to provide real-time DOA estimates for the detected sources. This algorithm was partitioned into several stages with some stages implemented in custom logic while others were implemented as software running on the soft-core processor. Just as with SCST, each partition of this beamforming algorithm was verified independently and then a full system test was conducted to evaluate whether it would be able to track an airborne source. For the full system test, a model airplane was flown at various trajectories…
Advisors/Committee Members: Azimi-Sadjadi, Mahmood R. (advisor), Pezeshki, Ali (committee member), Anderson, Charles (committee member).
Subjects/Keywords: noise monitoring; statistical signal processing; embedded systems; transient signals; remote sensing
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APA (6th Edition):
Yaremenko, V. (2017). Unattended acoustic sensor systems for noise monitoring in national parks. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/181332
Chicago Manual of Style (16th Edition):
Yaremenko, Vladimir. “Unattended acoustic sensor systems for noise monitoring in national parks.” 2017. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/181332.
MLA Handbook (7th Edition):
Yaremenko, Vladimir. “Unattended acoustic sensor systems for noise monitoring in national parks.” 2017. Web. 27 Feb 2021.
Vancouver:
Yaremenko V. Unattended acoustic sensor systems for noise monitoring in national parks. [Internet] [Masters thesis]. Colorado State University; 2017. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/181332.
Council of Science Editors:
Yaremenko V. Unattended acoustic sensor systems for noise monitoring in national parks. [Masters Thesis]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/181332
Colorado State University
25.
Sobhani, Amin.
P300 classification using deep belief nets.
Degree: MS(M.S.), Computer Science, 2014, Colorado State University
URL: http://hdl.handle.net/10217/84142
► Electroencephalogram (EEG) is measure of the electrical activity of the brain. One of the most important EEG paradigm that has been explored in BCI systems…
(more)
▼ Electroencephalogram (EEG) is measure of the electrical activity of the brain. One of the most important EEG paradigm that has been explored in BCI systems is the P300 signal. The P300 wave is an endogenous event-related-potential which can be captured during the process of decision making as a subject reacts to a stimulus. One way to detect the P300 signal is to show a subject two types of visual stimuli occurring at different rates. The event occurring less frequently than the other elicits a positive signal component with a latency of roughly 250-500 ms. P300 detection has many applications in the BCI field. One of the most common applications of P300 detection is the P300 speller which enables users to type letters on the screen. Machine Learning algorithms play a crucial role in designing a BCI system. One important purpose of using the machine learning algorithms in BCI systems is the classification of EEG signals. In order to translate EEG signals to a control signal, BCI systems should first capture the pattern of EEG signals and discriminate them into different command categories. This is usually done using different machine learning-based classifiers. In the past, different linear and nonlinear methods have been used to discriminate the P300 signals from nonP300 signals. This thesis provides the first attempt to implement and examine the performance of the Deep Belief Networks (DBN) to model the P300 data for classification. The highest classification accuracy we achieved with DBN is 97 percent for testing trials. In our experiments, we used EEG data collected by the BCI lab at
Colorado State University on both healthy and disabled subjects.
Advisors/Committee Members: Anderson, Charles (advisor), Ben-Hur, Asa (committee member), Peterson, Chris (committee member).
Subjects/Keywords: brain computer interface; P300 classification; machine learning; deep belief networks; EEG
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APA (6th Edition):
Sobhani, A. (2014). P300 classification using deep belief nets. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/84142
Chicago Manual of Style (16th Edition):
Sobhani, Amin. “P300 classification using deep belief nets.” 2014. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/84142.
MLA Handbook (7th Edition):
Sobhani, Amin. “P300 classification using deep belief nets.” 2014. Web. 27 Feb 2021.
Vancouver:
Sobhani A. P300 classification using deep belief nets. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/84142.
Council of Science Editors:
Sobhani A. P300 classification using deep belief nets. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/84142
Colorado State University
26.
Kumar, Shantanu.
Finding a solution for the tradeoff between time, cost and sustainability/LEED credits for new construction.
Degree: MS(M.S.), Construction Management, 2018, Colorado State University
URL: http://hdl.handle.net/10217/191361
► Project complexity generated tradeoffs in construction, which evolved over decades. This research focuses on the tradeoff between time-cost and sustainability represented in the LEED credits…
(more)
▼ Project complexity generated tradeoffs in construction, which evolved over decades. This research focuses on the tradeoff between time-cost and sustainability represented in the LEED credits (Materials and Resources in particular). The research was broken down into preliminary and validation studies, wherein the preliminary study used an exhaustive search to find the optimized solution. In validation case study, the size of dataset increased exponentially, and it became computationally incompatible to find the optimized solution. Genetic Algorithm (GA) was hence used to find the optimized solution based on priority factors entered by the user. Usage of GA was validated using the preliminary study data and then applied to the validation study data. A tradeoff could be seen between the priority factors and the optimized solution. It was found that the optimization model was successful in minimizing the time and cost, concurrently maximizing the credits for a validation case study conducted for a real-life project.
Advisors/Committee Members: Mehany, Mohammed S. Hashem M. (advisor), Guggemos, Angela Acree (committee member), Anderson, Charles W. (committee member).
Subjects/Keywords: genetic algorithm; optimization; tradeoffs; LEED; construction; sustainability
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APA (6th Edition):
Kumar, S. (2018). Finding a solution for the tradeoff between time, cost and sustainability/LEED credits for new construction. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/191361
Chicago Manual of Style (16th Edition):
Kumar, Shantanu. “Finding a solution for the tradeoff between time, cost and sustainability/LEED credits for new construction.” 2018. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/191361.
MLA Handbook (7th Edition):
Kumar, Shantanu. “Finding a solution for the tradeoff between time, cost and sustainability/LEED credits for new construction.” 2018. Web. 27 Feb 2021.
Vancouver:
Kumar S. Finding a solution for the tradeoff between time, cost and sustainability/LEED credits for new construction. [Internet] [Masters thesis]. Colorado State University; 2018. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/191361.
Council of Science Editors:
Kumar S. Finding a solution for the tradeoff between time, cost and sustainability/LEED credits for new construction. [Masters Thesis]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/191361
Colorado State University
27.
Mussa, Awad A. Younis.
Quantifying the security risk of discovering and exploiting software vulnerabilities.
Degree: PhD, Computer Science, 2016, Colorado State University
URL: http://hdl.handle.net/10217/176641
► Most of the attacks on computer systems and networks are enabled by vulnerabilities in a software. Assessing the security risk associated with those vulnerabilities is…
(more)
▼ Most of the attacks on computer systems and networks are enabled by vulnerabilities in a software. Assessing the security risk associated with those vulnerabilities is important. Risk mod- els such as the Common Vulnerability Scoring System (CVSS), Open Web Application Security Project (OWASP) and Common Weakness Scoring System (CWSS) have been used to qualitatively assess the security risk presented by a vulnerability. CVSS metrics are the de facto standard and its metrics need to be independently evaluated. In this dissertation, we propose using a quantitative approach that uses an actual data, mathematical and statistical modeling, data analysis, and measurement. We have introduced a novel vulnerability discovery model, Folded model, that estimates the risk of vulnerability discovery based on the number of residual vulnerabilities in a given software. In addition to estimating the risk of vulnerabilities discovery of a whole system, this dissertation has furthermore introduced a novel metrics termed time to vulnerability discovery to assess the risk of an individual vulnerability discovery. We also have proposed a novel vulnerability exploitability risk measure termed Structural Severity. It is based on software properties, namely attack entry points, vulnerability location, the presence of the dangerous system calls, and reachability analysis. In addition to measurement, this dissertation has also proposed predicting vulnerability exploitability risk using internal software metrics. We have also proposed two approaches for evaluating CVSS Base metrics. Using the availability of exploits, we first have evaluated the performance of the CVSS Exploitability factor and have compared its performance to Microsoft (MS) rating system. The results showed that exploitability metrics of CVSS and MS have a high false positive rate. This finding has motivated us to conduct further investigation. To that end, we have introduced vulnerability reward programs (VRPs) as a novel ground truth to evaluate the CVSS Base scores. The results show that the notable lack of exploits for high severity vulnerabilities may be the result of prioritized fixing of vulnerabilities.
Advisors/Committee Members: Malaiya, Yashwant (advisor), Ray, Indrajit (committee member), Anderson, Charles W. (committee member), Vijayasarathy, Leo (committee member).
Subjects/Keywords: software security; vulnerabilities exploitation; vulnerability rewards program and time to vulnerability disclosure; software vulnerabilities; cvss and OWASP metrics; vulnerabilities risk and severity
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APA (6th Edition):
Mussa, A. A. Y. (2016). Quantifying the security risk of discovering and exploiting software vulnerabilities. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/176641
Chicago Manual of Style (16th Edition):
Mussa, Awad A Younis. “Quantifying the security risk of discovering and exploiting software vulnerabilities.” 2016. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/176641.
MLA Handbook (7th Edition):
Mussa, Awad A Younis. “Quantifying the security risk of discovering and exploiting software vulnerabilities.” 2016. Web. 27 Feb 2021.
Vancouver:
Mussa AAY. Quantifying the security risk of discovering and exploiting software vulnerabilities. [Internet] [Doctoral dissertation]. Colorado State University; 2016. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/176641.
Council of Science Editors:
Mussa AAY. Quantifying the security risk of discovering and exploiting software vulnerabilities. [Doctoral Dissertation]. Colorado State University; 2016. Available from: http://hdl.handle.net/10217/176641
Colorado State University
28.
Yeluri, Sri Sagar Abhishek.
Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters.
Degree: MS(M.S.), Computer Science, 2020, Colorado State University
URL: http://hdl.handle.net/10217/212032
► In a world where Machine Learning Algorithms in the field of Image Processing is being developed at a rapid pace, a developer needs to have…
(more)
▼ In a world where Machine Learning Algorithms in the field of Image Processing is being developed at a rapid pace, a developer needs to have a better insight into all the algorithms to choose one among them for their application. When an algorithm is published, the developers of the algorithm compare their algorithm with already available well-performing algorithms and claim their algorithm outperforms all or the majority of other algorithms in terms of accuracy. However, adaptability is a very important aspect of Machine Learning which is usually not mentioned in their papers. Adaptability is the ability of a Machine Learning algorithm to work reliably in the real world, despite the change in the environmental factors in comparison to the environment in which data used for training is recorded. A machine learning algorithm that can give good results only on the dataset has no practical applications. In real life, the application of the algorithm increases only when it is more adaptable in nature. A few other aspects that are important in choosing the right algorithm for an application are consistency, time and resource utilization and the availability of human intervention. A person choosing amongst a list of algorithms for an application will be able to make a wise decision if given additional information, as each application varies from one another and needs a different set of characteristics of an algorithm for it to be well received. We have implemented and compared three Machine Learning algorithms used in image processing, on two different datasets and compare the results. We observe that certain algorithms, even though better than others in terms of accuracy on paper, fall behind when tested in real-world datasets. We put forward a few suggestions that if followed will simplify the selection of an algorithm for a specific purpose.
Advisors/Committee Members: Anderson, Charles W. (advisor), Beveridge, Ross (committee member), Hess, Ann (committee member).
Subjects/Keywords: machine learning; Siamese-like neural networks; neural networks; deep learning
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APA (6th Edition):
Yeluri, S. S. A. (2020). Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/212032
Chicago Manual of Style (16th Edition):
Yeluri, Sri Sagar Abhishek. “Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters.” 2020. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/212032.
MLA Handbook (7th Edition):
Yeluri, Sri Sagar Abhishek. “Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters.” 2020. Web. 27 Feb 2021.
Vancouver:
Yeluri SSA. Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/212032.
Council of Science Editors:
Yeluri SSA. Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/212032
Colorado State University
29.
Gorbett, Matthew.
Utilizing network features to detect erroneous inputs.
Degree: MS(M.S.), Computer Science, 2020, Colorado State University
URL: http://hdl.handle.net/10217/219573
► Neural networks are vulnerable to a wide range of erroneous inputs such as corrupted, out-of-distribution, misclassified, and adversarial examples. Previously, separate solutions have been proposed…
(more)
▼ Neural networks are vulnerable to a wide range of erroneous inputs such as corrupted, out-of-distribution, misclassified, and adversarial examples. Previously, separate solutions have been proposed for each of these faulty data types; however, in this work I show that the collective set of erroneous inputs can be jointly identified with a single model. Specifically, I train a linear SVM classifier to detect these four types of erroneous data using the hidden and softmax feature vectors of pre-trained neural networks. Results indicate that these faulty data types generally exhibit linearly separable activation properties from correctly processed examples. I am able to identify erroneous inputs with an AUROC of 0.973 on CIFAR10, 0.957 on Tiny ImageNet, and 0.941 on ImageNet. I experimentally validate the findings across a diverse range of datasets, domains, and pre-trained models.
Advisors/Committee Members: Blanchard, Nathaniel (advisor), Anderson, Charles W. (committee member), King, Emily (committee member).
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Chicago ·
MLA ·
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APA (6th Edition):
Gorbett, M. (2020). Utilizing network features to detect erroneous inputs. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/219573
Chicago Manual of Style (16th Edition):
Gorbett, Matthew. “Utilizing network features to detect erroneous inputs.” 2020. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/219573.
MLA Handbook (7th Edition):
Gorbett, Matthew. “Utilizing network features to detect erroneous inputs.” 2020. Web. 27 Feb 2021.
Vancouver:
Gorbett M. Utilizing network features to detect erroneous inputs. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/219573.
Council of Science Editors:
Gorbett M. Utilizing network features to detect erroneous inputs. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/219573
Colorado State University
30.
Barber, Michael J.
Classification ensemble methods for mitigating concept drift within online data streams.
Degree: MS(M.S.), Computer Science, 2012, Colorado State University
URL: http://hdl.handle.net/10217/67994
► The task of instance classification within very large data streams is challenged by both the overwhelming amount of data, and a phenomenon known as concept…
(more)
▼ The task of instance classification within very large data streams is challenged by both the overwhelming amount of data, and a phenomenon known as concept drift. In this research we provide a comprehensive comparison of several
state of the art ensemble methods that purport to handle concept drift, and we propose two additional algorithms. Our two new methods, the AMPE and AMPE2 algorithms are then used to further our understanding of concept drift and the algorithmic factors that influence the performance of ensemble based concept drift algorithms.
Advisors/Committee Members: Howe, Adele E. (advisor), Anderson, Charles (committee member), Hoeting, Jennifer (committee member).
Subjects/Keywords: data mining; online analysis; machine learning; ensembles
Record Details
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Record Details
Similar Records
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Barber, M. J. (2012). Classification ensemble methods for mitigating concept drift within online data streams. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/67994
Chicago Manual of Style (16th Edition):
Barber, Michael J. “Classification ensemble methods for mitigating concept drift within online data streams.” 2012. Masters Thesis, Colorado State University. Accessed February 27, 2021.
http://hdl.handle.net/10217/67994.
MLA Handbook (7th Edition):
Barber, Michael J. “Classification ensemble methods for mitigating concept drift within online data streams.” 2012. Web. 27 Feb 2021.
Vancouver:
Barber MJ. Classification ensemble methods for mitigating concept drift within online data streams. [Internet] [Masters thesis]. Colorado State University; 2012. [cited 2021 Feb 27].
Available from: http://hdl.handle.net/10217/67994.
Council of Science Editors:
Barber MJ. Classification ensemble methods for mitigating concept drift within online data streams. [Masters Thesis]. Colorado State University; 2012. Available from: http://hdl.handle.net/10217/67994
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