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You searched for +publisher:"Colorado State University" +contributor:("Anderson, Charles"). Showing records 1 – 30 of 66 total matches.

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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

 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)

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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

 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)

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

 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)

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

 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)

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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

 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)

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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

 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)

Subjects/Keywords: RFE; SVM; brain computer interface; machine learning; neural network; EEG

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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

 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)

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

 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)

Subjects/Keywords: epistemic uncertainty; uncertainty quantification; fuzzy sets; aleatory uncertainty

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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

 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)

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

 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)

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

 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)

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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

 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)

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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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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

 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)

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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

 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)

Subjects/Keywords: data mining; online analysis; machine learning; ensembles

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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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