+publisher:"Colorado State University" +contributor:("Anderson, Charles W.")

Colorado State University

1. 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 (6^th 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 (16^th Edition):

Trinko, David A. “Predictive energy management strategies for hybrid electric vehicles applied during acceleration events.” 2019. Masters Thesis, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/195328.

MLA Handbook (7^th Edition):

Trinko, David A. “Predictive energy management strategies for hybrid electric vehicles applied during acceleration events.” 2019. Web. 23 Jan 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 Jan 23]. 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

2. 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 (6^th 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 (16^th 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 January 23, 2021. http://hdl.handle.net/10217/65351.

MLA Handbook (7^th Edition):

Peterson, Erik J. “Many hats: intra-trial and reward-level dependent bold activity in the striatum and premotor cortex.” 2012. Web. 23 Jan 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 Jan 23]. 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

3. 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)

Subjects/Keywords: convolutional neural networks; feature space; machine learning; feature mapping; computer vision; ImageNet

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APA (6^th 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 (16^th 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 January 23, 2021. http://hdl.handle.net/10217/208467.

MLA Handbook (7^th Edition):

McNeely-White, David G. “Same data, same features: modern ImageNet-trained convolutional neural networks learn the same thing.” 2020. Web. 23 Jan 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 Jan 23]. 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

4. 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)

Subjects/Keywords: genetic algorithm; optimization; tradeoffs; LEED; construction; sustainability

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APA (6^th 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 (16^th 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 January 23, 2021. http://hdl.handle.net/10217/191361.

MLA Handbook (7^th Edition):

Kumar, Shantanu. “Finding a solution for the tradeoff between time, cost and sustainability/LEED credits for new construction.” 2018. Web. 23 Jan 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 Jan 23]. 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

5. 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 (6^th 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 (16^th 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 January 23, 2021. http://hdl.handle.net/10217/212032.

MLA Handbook (7^th Edition):

Yeluri, Sri Sagar Abhishek. “Classification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters.” 2020. Web. 23 Jan 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 Jan 23]. 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

6. 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Mussa, Awad A Younis. “Quantifying the security risk of discovering and exploiting software vulnerabilities.” 2016. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/176641.

MLA Handbook (7^th Edition):

Mussa, Awad A Younis. “Quantifying the security risk of discovering and exploiting software vulnerabilities.” 2016. Web. 23 Jan 2021.

Vancouver:

Mussa AAY. Quantifying the security risk of discovering and exploiting software vulnerabilities. [Internet] [Doctoral dissertation]. Colorado State University; 2016. [cited 2021 Jan 23]. 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

7. Rajasree, Revathy. Extending and validating the stencil processing unit.

Degree: MS(M.S.), Electrical and Computer Engineering, 2016, Colorado State University

URL: http://hdl.handle.net/10217/176694

► Stencils are an important class of programs that appear in the core of many scientific and general-purpose applications. These compute-intensive kernels can benefit heavily from… (more)

▼ Stencils are an important class of programs that appear in the core of many scientific and general-purpose applications. These compute-intensive kernels can benefit heavily from the massive compute power of accelerators like the GPGPU. However, due to the absence of any form of on-chip communication between the coarse-grain processors on a GPU, any data transfer/synchronization between the dependent tiles in stencil computations has to happen through the off-chip (global) memory, which is quite energy-expensive. In the road to exascale computing, energy is becoming an important cost metric. The need for hardware and software that can collaboratively work towards reducing energy consumption of a system is becoming more and more important. To make the execution of dense stencils more energy efficient, Rajopadhye et al. proposed the GPGPU-based accelerator called Stencil Processing Unit that introduces a simple neighbor-to-neighbor communication between the Streaming Multiprocessors (SM) on the GPU, thereby allowing some restricted data sharing between consecutive threadblocks. The SPU includes special storage units, called Communication Buffers, to orchestrate this data transfer and also provides an explicit mechanism for inter-threadblock synchronization by way of a special instruction. It claims to achieve energy-efficiency, compared to GPUs, by reducing the number of off-chip accesses in stencils which in turn reduces the dynamic energy overhead. Uguen developed a cycle-accurate performance simulator for the SPU, called SPU-Sim, and evaluated it using a matrix multiplication kernel which was not suitable for this accelerator. This work focuses on extending the SPU-Sim and evaluating the SPU architecture using a more insightful benchmark. We introduce a producer-consumer based inter-block synchronization approach on the SPU, which is more efficient than the previous global synchronization, and an overlapped multi-pass execution model in the SPU runtime system. These optimizations have been implemented into SPU-Sim. Furthermore, the existing GPUWattch power model in the simulator has been refined to provide better power estimates for the SPU architecture. The improved architecture has been evaluated using a simple 2-D stencil benchmark and we observe an average of 16% savings in dynamic energy on SPU compared to a fairly close GPU platform. Nonetheless, the total energy consumption on SPU is still comparatively high due to the static energy component. This high static energy on SPU is a direct impact of the increased leakage power of the platform resulting from the inclusion of special load/store units. Our conservative estimates indicate that replacing the current design of these L/S units with DMA engines can bring about a 15% decrease in the current leakage power of the SPU and this can help SPU outperform GPU in terms of energy. Advisors/Committee Members: Rajopdhye, Sanjay (advisor), Pasricha, Sudeep (committee member), Anderson, Charles W. (committee member).

Subjects/Keywords: CUDA; GPGPU; stencil; energy-efficiency; accelerator; multi-pass

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APA (6^th Edition):

Rajasree, R. (2016). Extending and validating the stencil processing unit. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/176694

Chicago Manual of Style (16^th Edition):

Rajasree, Revathy. “Extending and validating the stencil processing unit.” 2016. Masters Thesis, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/176694.

MLA Handbook (7^th Edition):

Rajasree, Revathy. “Extending and validating the stencil processing unit.” 2016. Web. 23 Jan 2021.

Vancouver:

Rajasree R. Extending and validating the stencil processing unit. [Internet] [Masters thesis]. Colorado State University; 2016. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/176694.

Council of Science Editors:

Rajasree R. Extending and validating the stencil processing unit. [Masters Thesis]. Colorado State University; 2016. Available from: http://hdl.handle.net/10217/176694

Colorado State University

8. Alzahrani, Saleh Ibrahim. P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors.

Degree: MS(M.S.), Bioengineering, 2016, Colorado State University

URL: http://hdl.handle.net/10217/178958

► Brain-computer interfaces (BCIs) allow interactions between human beings and comput- ers without using voluntary muscle. Enormous research effort has been employed in the last few… (more)

Subjects/Keywords: P300; Emotiv EPOC+; BCI; EEG

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APA (6^th Edition):

Alzahrani, S. I. (2016). P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/178958

Chicago Manual of Style (16^th Edition):

Alzahrani, Saleh Ibrahim. “P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors.” 2016. Masters Thesis, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/178958.

MLA Handbook (7^th Edition):

Alzahrani, Saleh Ibrahim. “P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors.” 2016. Web. 23 Jan 2021.

Vancouver:

Alzahrani SI. P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors. [Internet] [Masters thesis]. Colorado State University; 2016. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/178958.

Council of Science Editors:

Alzahrani SI. P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors. [Masters Thesis]. Colorado State University; 2016. Available from: http://hdl.handle.net/10217/178958

Colorado State University

9. Jaksic, Aleksandar. Design and evaluation of the FAMILIAR tool.

Degree: MS(M.S.), Computer Science, 2014, Colorado State University

URL: http://hdl.handle.net/10217/82556

► Software Product Line Engineering (SPLE) aims to efficiently produce multiple software products, on a large scale, that share a common set of core development features.… (more)

Subjects/Keywords: feature models; feature modeling; FAMILIAR; FAMILIAR tool; software product lines

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APA (6^th Edition):

Jaksic, A. (2014). Design and evaluation of the FAMILIAR tool. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/82556

Chicago Manual of Style (16^th Edition):

Jaksic, Aleksandar. “Design and evaluation of the FAMILIAR tool.” 2014. Masters Thesis, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/82556.

MLA Handbook (7^th Edition):

Jaksic, Aleksandar. “Design and evaluation of the FAMILIAR tool.” 2014. Web. 23 Jan 2021.

Vancouver:

Jaksic A. Design and evaluation of the FAMILIAR tool. [Internet] [Masters thesis]. Colorado State University; 2014. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/82556.

Council of Science Editors:

Jaksic A. Design and evaluation of the FAMILIAR tool. [Masters Thesis]. Colorado State University; 2014. Available from: http://hdl.handle.net/10217/82556

Colorado State University

10. Saripalli, Venkata Ratnam. Scalable and data efficient deep reinforcement learning methods for healthcare applications.

Degree: PhD, Systems Engineering, 2019, Colorado State University

URL: http://hdl.handle.net/10217/199875

► Artificial intelligence driven medical devices have created the potential for significant breakthroughs in healthcare technology. Healthcare applications using reinforcement learning are still very sparse as… (more)

▼ Artificial intelligence driven medical devices have created the potential for significant breakthroughs in healthcare technology. Healthcare applications using reinforcement learning are still very sparse as the medical domain is very complex and decision making requires domain expertise. High volumes of data generated from medical devices – a key input for delivering on the promise of AI, suffers from both noise and lack of ground truth. The cost of data increases as it is cleaned and annotated. Unlike other data sets, medical data annotation, which is critical for accurate ground truth, requires medical domain expertise for a high-quality patient outcome. While accurate recommendation of decisions is vital in this context, making them in near real-time on devices with computational resource constraint requires that we build efficient, compact representations of models such as deep neural networks. While deeper and wider neural networks are designed for complex healthcare applications, model compression can be an effective way to deploy networks on medical devices that often have hardware and speed constraints. Most state-of-the-art model compression techniques require a resource centric manual process that explores a large model architecture space to find a trade-off solution between model size and accuracy. Recently, reinforcement learning (RL) approaches are proposed to automate such a hand-crafted process. However, most RL model compression algorithms are model-free which require longer time with no assumptions of the model. On the contrary, model-based (MB) approaches are data driven; have faster convergence but are sensitive to the bias in the model. In this work, we report on the use of reinforcement learning to mimic the decision-making process of annotators for medical events, to automate annotation and labelling. The reinforcement agent learns to annotate alarm data based on annotations done by an expert. Our method shows promising results on medical alarm data sets. We trained deep Q-network and advantage actor-critic agents using the data from monitoring devices that are annotated by an expert. Initial results from these RL agents learning the expert-annotated behavior are encouraging and promising. The advantage actor-critic agent performs better in terms of learning the sparse events in a given state, thereby choosing more right actions compared to deep Q-network agent. To the best of our knowledge, this is the first reinforcement learning application for the automation of medical events annotation, which has far-reaching practical use. In addition, a data-driven model-based algorithm is developed, which integrates seamlessly with model-free RL approaches for automation of deep neural network model compression. We evaluate our algorithm on a variety of imaging data from dermoscopy to X-ray on different popular and public model architectures. Compared to model-free RL approaches, our approach achieves faster convergence; exhibits better generalization across different data sets; and preserves comparable… Advisors/Committee Members: Anderson, Charles W. (advisor), Hess, Ann Marie (committee member), Young, Peter (committee member), Simske, Steve John (committee member).

Subjects/Keywords: artificial intelligence; AI assisted annotation; reinforcement learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Saripalli, V. R. (2019). Scalable and data efficient deep reinforcement learning methods for healthcare applications. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/199875

Chicago Manual of Style (16^th Edition):

Saripalli, Venkata Ratnam. “Scalable and data efficient deep reinforcement learning methods for healthcare applications.” 2019. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/199875.

MLA Handbook (7^th Edition):

Saripalli, Venkata Ratnam. “Scalable and data efficient deep reinforcement learning methods for healthcare applications.” 2019. Web. 23 Jan 2021.

Vancouver:

Saripalli VR. Scalable and data efficient deep reinforcement learning methods for healthcare applications. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/199875.

Council of Science Editors:

Saripalli VR. Scalable and data efficient deep reinforcement learning methods for healthcare applications. [Doctoral Dissertation]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/199875

Colorado State University

11. Rieker, Jeffrey Donald. Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy.

Degree: PhD, Civil and Environmental Engineering, 2011, Colorado State University

URL: http://hdl.handle.net/10217/49858

► Complex water resources systems often involve a wide variety of competing objectives and purposes, including the improvement of water quality downstream of reservoirs. An increased… (more)

▼ Complex water resources systems often involve a wide variety of competing objectives and purposes, including the improvement of water quality downstream of reservoirs. An increased focus on downstream water quality considerations in the operating strategies for reservoirs has given impetus to the need for tools to assist water resource managers in developing strategies for release of water for downstream water quality improvement, while considering other important project purposes. This study applies an artificial intelligence methodology known as reinforcement learning to the operation of reservoir systems for water quality enhancement through augmentation of instream flow. Reinforcement learning is a methodology that employs the concepts of agent control and evaluative feedback to develop improved reservoir operating strategies through direct interaction with a simulated river and reservoir environment driven by stochastic hydrology. Reinforcement learning methods have advantages over other more traditional stochastic optimization methods through implicit learning of the underlying stochastic structure through interaction with the simulated environment, rather than requiring a priori specification of probabilistic models. Reinforcement learning can also be coupled with various computing efficiency techniques as well as other machine learning methods such as artificial neural networks to mitigate the "curse of dimensionality" that is common to many optimization methodologies for solving sequential decision problems. A generalized mechanism is developed, tested, and evaluated for providing near-real time operational support to suggest releases of water from upstream reservoirs to improve water quality within a river using releases specifically designated for that purpose. The algorithm is designed to address a variable number of water quality constituents, with additional flexibility for adding new water quality requirements and learning updated operating strategies in a non-stationary environment. The generalized reinforcement learning algorithm is applied to the Truckee River in California and Nevada as a case study, where the federal and local governments are purchasing water rights for the purpose of augmenting Truckee River flows to improve water quality. Water associated with those acquired rights can be stored in upstream reservoirs on the Truckee River until needed for prevention of water quality standard violations in the lower reaches of the river. This study shows that in order for the water acquired for flow augmentation to be fully utilized as a part of a longer-term strategy for water quality management, increased flexibility is required as to how those waters are stored and how well the storage is protected from displacement through reservoir spill during times of high runoff. The results show that with those flexibilities, the reinforcement learning mechanism has the ability to produce both short-term and long-term strategies for the use of the water, with the long-term strategies capable… Advisors/Committee Members: Labadie, John W. (advisor), Fontane, Darrell G. (committee member), Frevert, Donald K. (committee member), Anderson, Charles W. (committee member).

Subjects/Keywords: reinforcement learning; truckee; reservoir operations; artificial intelligence; optimization; water quality

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APA (6^th Edition):

Rieker, J. D. (2011). Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/49858

Chicago Manual of Style (16^th Edition):

Rieker, Jeffrey Donald. “Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy.” 2011. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/49858.

MLA Handbook (7^th Edition):

Rieker, Jeffrey Donald. “Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy.” 2011. Web. 23 Jan 2021.

Vancouver:

Rieker JD. Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy. [Internet] [Doctoral dissertation]. Colorado State University; 2011. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/49858.

Council of Science Editors:

Rieker JD. Optimal reservoir operations for riverine water quality improvement: a reinforcement learning strategy. [Doctoral Dissertation]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/49858

Colorado State University

12. Lee, Minwoo. Sparse Bayesian reinforcement learning.

Degree: PhD, Computer Science, 2017, Colorado State University

URL: http://hdl.handle.net/10217/183935

► This dissertation presents knowledge acquisition and retention methods for efficient and robust learning. We propose a framework for learning and memorizing, and we examine how… (more)

▼ This dissertation presents knowledge acquisition and retention methods for efficient and robust learning. We propose a framework for learning and memorizing, and we examine how we can use the memory for efficient machine learning. Temporal difference (TD) learning is a core part of reinforcement learning, and it requires function approximation. However, with function approximation, the most popular TD methods such as TD(λ), SARSA, and Q-learning lose stability and diverge especially when the complexity of the problem grows and the sampling distribution is biased. The biased samples cause function approximators such as neural networks to respond quickly to the new data by losing what was previously learned. Systematically selecting a most significant experience, our proposed approach gradually stores the snapshot memory. The memorized snapshots prevent forgetting important samples and increase learning stability. Our sparse Bayesian learning model maintains the sparse snapshot memory for efficiency in computation and memory. The Bayesian model extends and improves TD learning by utilizing the state information in hyperparameters for smart decision of action selection and filtering insignificant experience to maintain sparsity of snapshots for efficiency. The obtained memory can be used to further improve learning. First, the placement of the snapshot memories with a radial basis function kernel located at peaks of the value function approximation surface leads to an efficient way to search a continuous action space for practical application with fine motor control. Second, the memory is a knowledge representation for transfer learning. Transfer learning is a paradigm for knowledge generalization of machine learning and reinforcement learning. Transfer learning shortens the time for machine learning training by using the knowledge gained from similar tasks. The dissertation examines a practice approach that transfers the snapshots from non-goal-directive random movements to goal-driven reinforcement learning tasks. Experiments are described that demonstrate the stability and efficiency of learning in 1) traditional benchmark problems and 2) the octopus arm control problem without limiting or discretizing the action space. Advisors/Committee Members: Anderson, Charles W. (advisor), Ben-Hur, Asa (committee member), Kirby, Michael (committee member), Young, Peter (committee member).

Subjects/Keywords: continuous action space; practice; sparse learning; knowledge retention; Bayesian learning; reinforcement learning

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APA (6^th Edition):

Lee, M. (2017). Sparse Bayesian reinforcement learning. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/183935

Chicago Manual of Style (16^th Edition):

Lee, Minwoo. “Sparse Bayesian reinforcement learning.” 2017. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/183935.

MLA Handbook (7^th Edition):

Lee, Minwoo. “Sparse Bayesian reinforcement learning.” 2017. Web. 23 Jan 2021.

Vancouver:

Lee M. Sparse Bayesian reinforcement learning. [Internet] [Doctoral dissertation]. Colorado State University; 2017. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/183935.

Council of Science Editors:

Lee M. Sparse Bayesian reinforcement learning. [Doctoral Dissertation]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/183935

Colorado State University

13. Tan, Haiming. Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars.

Degree: MS(M.S.), Computer Science, 2019, Colorado State University

URL: http://hdl.handle.net/10217/195325

► Precipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation… (more)

▼ Precipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential. Advisors/Committee Members: Anderson, Charles W. (advisor), Chandra, Chandrasekar V. (advisor), Ray, Indrajit (committee member), Chavez, Jose L. (committee member).

Subjects/Keywords: rainfall estimation; machine learning; spaceborne radar

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APA (6^th Edition):

Tan, H. (2019). Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/195325

Chicago Manual of Style (16^th Edition):

Tan, Haiming. “Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars.” 2019. Masters Thesis, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/195325.

MLA Handbook (7^th Edition):

Tan, Haiming. “Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars.” 2019. Web. 23 Jan 2021.

Vancouver:

Tan H. Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/195325.

Council of Science Editors:

Tan H. Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/195325

Colorado State University

14. Rohmat, Faizal Immaddudin Wira. Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law.

Degree: PhD, Civil and Environmental Engineering, 2019, Colorado State University

URL: http://hdl.handle.net/10217/197364

► The sustainability issues facing irrigated river basins are intensified by legal and institutional regulations imposed on the hydrologic system. Although solutions that would boost water… (more)

▼ The sustainability issues facing irrigated river basins are intensified by legal and institutional regulations imposed on the hydrologic system. Although solutions that would boost water savings and quality might prove to be feasible, such imposed institutional constraints could veto their implementation, rendering them legally ineffectual. The problems of basin-scale irrigation water management in a legally-constrained system are exemplified in the central alluvial valley of the Lower Arkansas River Basin (LARB) in Colorado, USA, and in the Tripa River Basin in Indonesia. In the LARB, water and land best management practices (BMPs) have been proposed to enhance the environment, conserve water, and boost productivity; however, the legal feasibility of their implementation in the basin hinder BMP adoption. In the Tripa river basin, the rapid growth of water demand pushes the proposal of new reservoir construction. However, inadequate water availability and the lack of water law enforcement requires the basin to seek water from adjacent basins, thereby raising legal and economic feasibility issues. To address these issues, an updated version of a decision support system (DSS) named River GeoDSS has been employed to model basin-scale behavior of the LARB for both historical (baseline) and BMP implementation scenarios. River GeoDSS uses GeoMODSIM as its water allocation component, which also handles water rights and uses a deep neural network (DNN) functionality to emulate calibrated regional MODFLOW-SFR2 models in modeling complex stream-aquifer interactions. The use of DNNs for emulation if critical for extrapolating the results of MODFLOW-SFR2 simulations to un-modeled portions of the basin and for compute-efficient analysis. The BMP implementations are found to introduce significant alterations to streamflows in the LARB, including shortages in flow deliveries to water right demands and in flow deficits at the Colorado-Kansas Stateline. To address this, an advanced Fuzzy-Mutation Linear Particle Swarm Optimization (Fuzzy-MLPSO) metaheuristic algorithm is applied to determine optimal operational policies for a new storage account in John Martin Reservoir for use in mitigating the side-effects of BMP implementation on water rights and the interstate compact. Prior to the implementation of Fuzzy-MLPSO, a dedicated study is conducted to develop the integration between MLPSO and GeoMODSIM, where it is applied in addressing the water allocation issue in the Tripa River Basin. The coupling of simulation (GeoMODSIM) and optimization (MLPSO) models provides optimal sizing of reservoirs and transbasin diversions along with optimal operation policies. Aside from that, this study shows that MLPSO converges faster compared to the original PSO with sufficiently smaller swarm size. The implementations of Fuzzy-MLPSO in the LARB provided optimal operational rules for a new storage account in John Martin Reservoir dedicated to abating the undesirable impacts of BMP implementation on water rights and Stateline flows. The… Advisors/Committee Members: Labadie, John W. (advisor), Gates, Timothy K. (advisor), Bailey, Ryan T. (committee member), Anderson, Charles W. (committee member).

Subjects/Keywords: machine learning; reservoir operation; stream-aquifer systems; particle swarm optimization; fuzzy logic; river basin management

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APA (6^th Edition):

Rohmat, F. I. W. (2019). Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/197364

Chicago Manual of Style (16^th Edition):

Rohmat, Faizal Immaddudin Wira. “Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law.” 2019. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/197364.

MLA Handbook (7^th Edition):

Rohmat, Faizal Immaddudin Wira. “Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law.” 2019. Web. 23 Jan 2021.

Vancouver:

Rohmat FIW. Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/197364.

Council of Science Editors:

Rohmat FIW. Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law. [Doctoral Dissertation]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/197364

15. Oikonomou, Panagiotis D. Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management.

Degree: PhD, Civil and Environmental Engineering, 2017, Colorado State University

URL: http://hdl.handle.net/10217/183883

► The majority of river basins in the world, have undergone a great deal of transformations in terms of infrastructure and water management practices in order… (more)

▼ The majority of river basins in the world, have undergone a great deal of transformations in terms of infrastructure and water management practices in order to meet increasing water needs due to population growth and socio-economic development. Surface water and groundwater systems are interwoven with environmental and socio-economic ones. The systems' dynamic nature, their complex interlinkages and interdependencies are inducing challenges for integrated water resources management. Informed decision-making process in water resources is deriving from a systematic analysis of the available data with the utilization of tools and models, by examining viable alternatives and their associated tradeoffs under the prism of a set of prudent priorities and expert knowledge. In an era of increasing volume and variety of data about natural and anthropogenic systems, opportunities arise for further enhancing data integration in problem-solving approaches and thus support decision-making for water resources planning and management. Although there is a plethora of variables monitored in various spatial and temporal scales, particularly in the United States, in real life, for water resources applications there are rarely, if ever, perfect data. Developing more systematic procedures to integrate the available data and harness their full potential of generating information, will improve the understanding of water resources systems and assist at the same time integrated water resources management efforts. The overarching objective of this study is to develop tools and approaches to overcome data obstacles in water resources management. This required the development of methodologies that utilize a wide range of water and environmental datasets in order to transform them into reliable and valuable information, which would address unanswered questions about water systems and water management practices, contributing to implementable efforts of integrated water resources management. More specifically, the objectives of this research are targeted in three complementary topics: drought, water demand, and groundwater supply. In this regard, their unified thread is the common quest for integrated river basin management (IRBM) under changing water resources conditions. All proposed methodologies have a common area of application namely the South Platte basin, located within Colorado. The area is characterized by limited water resources with frequent drought intervals. A system's vulnerability to drought due to the different manifestations of the phenomenon (meteorological, agricultural, hydrological, socio-economic and ecological) and the plethora of factors affecting it (precipitation patterns, the supply and demand trends, the socioeconomic background etc.) necessitates an integrated approach for delineating its magnitude and spatiotemporal extent and impacts. Thus, the first objective was to develop an implementable drought management policy tool based on the standardized drought vulnerability index framework and expanding it in order to… Advisors/Committee Members: Fontane, Darrell G. (advisor), Waskom, Reagan M. (advisor), Grigg, Neil S. (committee member), Karavitis, Christos A. (committee member), Anderson, Charles W. (committee member).

Subjects/Keywords: drought vulnerability; groundwater level gap-filling methodology; Weld and Garfield counties; drought; unconventional oil and gas water demand; ensemble smoother; Colorado

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APA (6^th Edition):

Oikonomou, P. D. (2017). Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/183883

Chicago Manual of Style (16^th Edition):

Oikonomou, Panagiotis D. “Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management.” 2017. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/183883.

MLA Handbook (7^th Edition):

Oikonomou, Panagiotis D. “Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management.” 2017. Web. 23 Jan 2021.

Vancouver:

Oikonomou PD. Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management. [Internet] [Doctoral dissertation]. Colorado State University; 2017. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/183883.

Council of Science Editors:

Oikonomou PD. Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management. [Doctoral Dissertation]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/183883

Colorado State University

16. Narayana, Pradyumna. Improving gesture recognition through spatial focus of attention.

Degree: PhD, Computer Science, 2018, Colorado State University

URL: http://hdl.handle.net/10217/193149

► Gestures are a common form of human communication and important for human computer interfaces (HCI). Most recent approaches to gesture recognition use deep learning within… (more)

▼ Gestures are a common form of human communication and important for human computer interfaces (HCI). Most recent approaches to gesture recognition use deep learning within multi- channel architectures. We show that when spatial attention is focused on the hands, gesture recognition improves significantly, particularly when the channels are fused using a sparse network. We propose an architecture (FOANet) that divides processing among four modalities (RGB, depth, RGB flow, and depth flow), and three spatial focus of attention regions (global, left hand, and right hand). The resulting 12 channels are fused using sparse networks. This architecture improves performance on the ChaLearn IsoGD dataset from a previous best of 67.71% to 82.07%, and on the NVIDIA dynamic hand gesture dataset from 83.8% to 91.28%. We extend FOANet to perform gesture recognition on continuous streams of data. We show that the best temporal fusion strategies for multi-channel networks depends on the modality (RGB vs depth vs flow field) and target (global vs left hand vs right hand) of the channel. The extended architecture achieves optimum performance using Gaussian Pooling for global channels, LSTMs for focused (left hand or right hand) flow field channels, and late Pooling for focused RGB and depth channels. The resulting system achieves a mean Jaccard Index of 0.7740 compared to the previous best result of 0.6103 on the ChaLearn ConGD dataset without first pre-segmenting the videos into single gesture clips. Human vision has α and β channels for processing different modalities in addition to spatial attention similar to FOANet. However, unlike FOANet, attention is not implemented through separate neural channels. Instead, attention is implemented through top-down excitation of neurons corresponding to specific spatial locations within the α and β channels. Motivated by the covert attention in human vision, we propose a new architecture called CANet (Covert Attention Net), that merges spatial attention channels while preserving the concept of attention. The focus layers of CANet allows it to focus attention on hands without having dedicated attention channels. CANet outperforms FOANet by achieving an accuracy of 84.79% on ChaLearn IsoGD dataset while being efficient (≈35% of FOANet parameters and ≈70% of FOANet operations). In addition to producing state-of-the-art results on multiple gesture recognition datasets, this thesis also tries to understand the behavior of multi-channel networks (a la FOANet). Multi- channel architectures are becoming increasingly common, setting the state of the art for performance in gesture recognition and other domains. Unfortunately, we lack a clear explanation of why multi-channel architectures outperform single channel ones. This thesis considers two hypotheses. The Bagging hypothesis says that multi-channel architectures succeed because they average the result of multiple unbiased weak estimators in the form of different channels. The Society of Experts (SoE) hypothesis suggests that multi-channel… Advisors/Committee Members: Draper, Bruce A. (advisor), Beveridge, Ross J. (committee member), Anderson, Charles W. (committee member), Peterson, Christopher (committee member).

Subjects/Keywords: focus of attention; network fusion; gesture recognition; deep learning

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APA (6^th Edition):

Narayana, P. (2018). Improving gesture recognition through spatial focus of attention. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/193149

Chicago Manual of Style (16^th Edition):

Narayana, Pradyumna. “Improving gesture recognition through spatial focus of attention.” 2018. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/193149.

MLA Handbook (7^th Edition):

Narayana, Pradyumna. “Improving gesture recognition through spatial focus of attention.” 2018. Web. 23 Jan 2021.

Vancouver:

Narayana P. Improving gesture recognition through spatial focus of attention. [Internet] [Doctoral dissertation]. Colorado State University; 2018. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/193149.

Council of Science Editors:

Narayana P. Improving gesture recognition through spatial focus of attention. [Doctoral Dissertation]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/193149

Colorado State University

17. Ashari, Rehab Bahaaddin. EEG subspace analysis and classification using principal angles for brain-computer interfaces.

Degree: PhD, Computer Science, 2015, Colorado State University

URL: http://hdl.handle.net/10217/167006

► Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of… (more)

▼ Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only, should make this approach more appropriate for online BCI applications. In order to understand and test how EEG signals are different from one subject to another, different users are tested in this dissertation, some with motor impairments. Furthermore, the concept of transferring information between subjects is examined by training the approach on one subject and testing it on the other subject using the training subject's EEG subspaces to classify the testing subject's trials. Advisors/Committee Members: Anderson, Charles W. (advisor), Ben-Hur, Asa (committee member), Draper, Bruce (committee member), Peterson, Chris (committee member).

Subjects/Keywords: principal angles; brain computer interfaces; subspace

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APA (6^th Edition):

Ashari, R. B. (2015). EEG subspace analysis and classification using principal angles for brain-computer interfaces. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/167006

Chicago Manual of Style (16^th Edition):

Ashari, Rehab Bahaaddin. “EEG subspace analysis and classification using principal angles for brain-computer interfaces.” 2015. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/167006.

MLA Handbook (7^th Edition):

Ashari, Rehab Bahaaddin. “EEG subspace analysis and classification using principal angles for brain-computer interfaces.” 2015. Web. 23 Jan 2021.

Vancouver:

Ashari RB. EEG subspace analysis and classification using principal angles for brain-computer interfaces. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/167006.

Council of Science Editors:

Ashari RB. EEG subspace analysis and classification using principal angles for brain-computer interfaces. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167006

18. Elliott, Daniel L. Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The.

Degree: PhD, Computer Science, 2018, Colorado State University

URL: http://hdl.handle.net/10217/191477

► Reinforcement learning agents learn by exploring the environment and then exploiting what they have learned. This frees the human trainers from having to know the… (more)

Subjects/Keywords: machine learning; Q-learning; ensemble; reinforcement learning; neural networks

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APA (6^th Edition):

Elliott, D. L. (2018). Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/191477

Chicago Manual of Style (16^th Edition):

Elliott, Daniel L. “Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The.” 2018. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/191477.

MLA Handbook (7^th Edition):

Elliott, Daniel L. “Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The.” 2018. Web. 23 Jan 2021.

Vancouver:

Elliott DL. Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The. [Internet] [Doctoral dissertation]. Colorado State University; 2018. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/191477.

Council of Science Editors:

Elliott DL. Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The. [Doctoral Dissertation]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/191477

19. Sadeghi, Marjan. Information-augmented building information models (BIM) to inform facilities management (FM) guidelines.

Degree: PhD, Civil and Environmental Engineering, 2019, Colorado State University

URL: http://hdl.handle.net/10217/197250

► The asset portfolios of Higher Education Institutions (HEI) typically incorporate a highly diverse collection of buildings with various and often shared campus uses. These facilities… (more)

▼ The asset portfolios of Higher Education Institutions (HEI) typically incorporate a highly diverse collection of buildings with various and often shared campus uses. These facilities are typically at different points in their operational lifecycle, have different characteristics, rehabilitation cost, maintenance costs, and mission criticality. In the resource-constrained context of higher education Facilities Management (FM), building data for all facilities needs to be integrated within a highly-informed decision-making process to promote efficient operation. Further, efficient building FM workflows depend upon accurate, reliable, and timely information for various building-specific systems, components, and elements. Traditional Facilities Information Management (FIM) platforms and processes have been shown to be inefficient and limited for capturing and delivering the extensive and comprehensive data needed for FM decision making. Such inefficiencies include, but are not limited to, information loss, inconsistencies of the available data, and manual data re-entry at construction-to-operation handover and project close out. Building Information Models (BIMs) are capable of integrating large quantities of data and have been recognized as a compelling tool for facility life-cycle information management. BIMs provide an object-oriented, parametric, 3D environment where meaningful objects with intelligent behavior can contain geometric and non-geometric data. This capability makes BIMs a powerful tool for use beyond building visualization. Furthermore, BIM authoring tools are capable of automatically integrating data with FM technologies. Although BIMs have the potential to provide a compelling tool to capture, deliver, validate, retrieve, exchange, and analyze facility lifecycle information, implementation of BIMs for FM handover and integration within the context of FIM remains limited. A plethora of academic and industry efforts strive to address various aspects of BIM interoperability for handing over building data for implementation in post-construction building operation workflows. Attempts to incorporate BIMs in FIM have generally focused on one of five domains; what information is to be exchanged, how, when, by whom, and why. This three-manuscript dissertation explores FM handover information exchange scenarios and provides a comprehensive, object-oriented BIM solution that identifies the requirements for model content for FM- specific needs. The results formalize an appropriate process and structured framework to deliver BIM content using FM-specific terminologies and taxonomies. BIMs created for design and construction using this framework provide a suitable 3D resource for post-handover FM and building operation. The BIM development framework presented herein can facilitate automated model data validation at project close out and the exchange of AEC data with FIM systems. This modeling process can reduce the need for manual data re-entry or interpretation by FM stakeholders during building operation. This… Advisors/Committee Members: Grigg, Neil (advisor), Elliot, Jonathan W. (advisor), Mehany, Mohammed S. Hashem M. (committee member), Anderson, Charles W. (committee member).

Subjects/Keywords: building information modeling; industry foundation classes; level of semantics; facilities management; BIM execution plan; level of development

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APA (6^th Edition):

Sadeghi, M. (2019). Information-augmented building information models (BIM) to inform facilities management (FM) guidelines. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/197250

Chicago Manual of Style (16^th Edition):

Sadeghi, Marjan. “Information-augmented building information models (BIM) to inform facilities management (FM) guidelines.” 2019. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/197250.

MLA Handbook (7^th Edition):

Sadeghi, Marjan. “Information-augmented building information models (BIM) to inform facilities management (FM) guidelines.” 2019. Web. 23 Jan 2021.

Vancouver:

Sadeghi M. Information-augmented building information models (BIM) to inform facilities management (FM) guidelines. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/197250.

Council of Science Editors:

Sadeghi M. Information-augmented building information models (BIM) to inform facilities management (FM) guidelines. [Doctoral Dissertation]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/197250

20. Alotaibi, Saud Saleh. Sentiment analysis in the Arabic language using machine learning.

Degree: PhD, Computer Science, 2015, Colorado State University

URL: http://hdl.handle.net/10217/167091

► Sentiment analysis has recently become one of the growing areas of research related to natural language processing and machine learning. Much opinion and sentiment about… (more)

▼ Sentiment analysis has recently become one of the growing areas of research related to natural language processing and machine learning. Much opinion and sentiment about specific topics are available online, which allows several parties such as customers, companies and even governments, to explore these opinions. The first task is to classify the text in terms of whether or not it expresses opinion or factual information. Polarity classification is the second task, which distinguishes between polarities (positive, negative or neutral) that sentences may carry. The analysis of natural language text for the identification of subjectivity and sentiment has been well studied in terms of the English language. Conversely, the work that has been carried out in terms of Arabic remains in its infancy; thus, more cooperation is required between research communities in order for them to offer a mature sentiment analysis system for Arabic. There are recognized challenges in this field; some of which are inherited from the nature of the Arabic language itself, while others are derived from the scarcity of tools and sources. This dissertation provides the rationale behind the current work and proposed methods to enhance the performance of sentiment analysis in the Arabic language. The first step is to increase the resources that help in the analysis process; the most important part of this task is to have annotated sentiment corpora. Several free corpora are available for the English language, but these resources are still limited in other languages, such as Arabic. This dissertation describes the work undertaken by the author to enrich sentiment analysis in Arabic by building a new Arabic Sentiment Corpus. The data is labeled not only with two polarities (positive and negative), but the neutral sentiment is also used during the annotation process. The second step includes the proposal of features that may capture sentiment orientation in the Arabic language, as well as using different machine learning classifiers that may be able to work better and capture the non-linearity with a richly morphological and highly inflectional language, such as Arabic. Different types of features are proposed. These proposed features try to capture different aspects and characteristics of Arabic. Morphological, Semantic, Stylistic features are proposed and investigated. In regard with the classifier, the performance of using linear and nonlinear machine learning approaches was compared. The results are promising for the continued use of nonlinear ML classifiers for this task. Learning knowledge from a particular dataset domain and applying it to a different domain is one useful method in the case of limited resources, such as with the Arabic language. This dissertation shows and discussed the possibility of applying cross-domain in the field of Arabic sentiment analysis. It also indicates the feasibility of using different mechanisms of the cross-domain method. Other work in this dissertation includes the exploration of the effect of negation in… Advisors/Committee Members: Anderson, Charles W. (advisor), Ben-Hur, Asa (committee member), Ray, Indrakshi (committee member), Peterson, Chris (committee member).

Subjects/Keywords: machine learning; sentiment analysis; Arabic sentiment; subjectivity classification; polarity classification

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APA (6^th Edition):

Alotaibi, S. S. (2015). Sentiment analysis in the Arabic language using machine learning. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/167091

Chicago Manual of Style (16^th Edition):

Alotaibi, Saud Saleh. “Sentiment analysis in the Arabic language using machine learning.” 2015. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/167091.

MLA Handbook (7^th Edition):

Alotaibi, Saud Saleh. “Sentiment analysis in the Arabic language using machine learning.” 2015. Web. 23 Jan 2021.

Vancouver:

Alotaibi SS. Sentiment analysis in the Arabic language using machine learning. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/167091.

Council of Science Editors:

Alotaibi SS. Sentiment analysis in the Arabic language using machine learning. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167091

21. Alzahrani, Saleh Ibrahim. Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A.

Degree: PhD, Bioengineering, 2019, Colorado State University

URL: http://hdl.handle.net/10217/195380

► The electroencephalogram (EEG) is broadly used for diagnosis of brain diseases and research of brain activities. Although the EEG provides a good temporal resolution, it… (more)

▼ The electroencephalogram (EEG) is broadly used for diagnosis of brain diseases and research of brain activities. Although the EEG provides a good temporal resolution, it suffers from poor spatial resolution due to the blurring effects of volume conduction and signal-to-noise ratio. Many efforts have been devoted to the development of novel methods that can increase the EEG spatial resolution. The surface Laplacian, which is the second derivative of the surface potential, has been applied to EEG to improve the spatial resolution. Tri-polar concentric ring electrodes (TCREs) have been shown to estimate the surface Laplacian automatically with better spatial resolution than conventional disc electrodes. The aim of this research is to study how well the TCREs can be used to acquire EEG signals to decode real and imaginary finger movements. These EEG signals will be then translated into finger movements commands. We also compare the feasibility of discriminating finger movements from one hand using EEG recorded from TCREs and conventional disc electrodes. Furthermore, we evaluated two movement-related features, temporal EEG data and spectral features, in discriminating individual finger from one hand using non-invasive EEG. To do so, movement-related potentials (MRPs) are measured and analyzed from four TCREs and conventional disc electrodes while 13 subjects performed either motor execution or motor imagery of individual finger movements. The tri-polar-EEG (tEEG) and conventional EEG (cEEG) were recorded from electrodes placed according to the 10-20 International Electrode Positioning System over the motor cortex. Our results show that the TCREs achieved higher spatial resolution than conventional disc electrodes. Moreover, the results show that signals from TCREs generated higher decoding accuracy compared to signals from conventional disc electrodes. The average decoding accuracy of five-class classification for all subjects was of 70.04 ± 7.68% when we used temporal EEG data as feature and classified it using Artificial Neural Networks (ANNs) classifier. In addition, the results show that the TCRE EEG (tEEG) provides approximately a four times enhancement in the signal-to-noise ratio (SNR) compared to disc electrode signals. We also evaluated the interdependency level between neighboring electrodes from tri-polar, disc, and disc with Hjorth's Laplacian method in time and frequency domains by calculating the mutual information (MI) and coherence. The MRP signals recorded with the TCRE system have significantly less mutual information (MI) between electrodes than the conventional disc electrode system and disc electrodes with Hjorth's Laplacian method. Also, the results show that the mean coherence between neighboring tri-polar electrodes was found to be significantly smaller than disc electrode and disc electrode with Hjorth's method, especially at higher frequencies. This lower coherence in the high frequency band between neighboring tri polar electrodes suggests that the TCREs may record a more localized neuronal… Advisors/Committee Members: Anderson, Charles W. (advisor), Vigh, Jozsef (committee member), Rojas, Don (committee member), Abdel-Ghany, Salah (committee member).

Subjects/Keywords: brain-computer interface; TCRE; EEG

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APA (6^th Edition):

Alzahrani, S. I. (2019). Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/195380

Chicago Manual of Style (16^th Edition):

Alzahrani, Saleh Ibrahim. “Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A.” 2019. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/195380.

MLA Handbook (7^th Edition):

Alzahrani, Saleh Ibrahim. “Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A.” 2019. Web. 23 Jan 2021.

Vancouver:

Alzahrani SI. Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/195380.

Council of Science Editors:

Alzahrani SI. Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A. [Doctoral Dissertation]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/195380

Colorado State University

22. Teli, Mohammad Nayeem. Dimensionality reduction and classification of time embedded EEG signals.

Degree: MS(M.S.), Computer Science, 2007, Colorado State University

URL: http://hdl.handle.net/10217/28637

► Electroencephalogram (EEG) is the measurement of the electrical activity of the brain measured by placing electrodes on the scalp. These EEG signals give the micro-voltage… (more)

Subjects/Keywords: SVM; electroencephalogram; QDA; support vector machines; bottleneck neural network; linear discriminant analysis; LDA; quadratic discriminant analysis; Brain-computer interfaces; Electroencephalography; EEG

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APA (6^th Edition):

Teli, M. N. (2007). Dimensionality reduction and classification of time embedded EEG signals. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/28637

Chicago Manual of Style (16^th Edition):

Teli, Mohammad Nayeem. “Dimensionality reduction and classification of time embedded EEG signals.” 2007. Masters Thesis, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/28637.

MLA Handbook (7^th Edition):

Teli, Mohammad Nayeem. “Dimensionality reduction and classification of time embedded EEG signals.” 2007. Web. 23 Jan 2021.

Vancouver:

Teli MN. Dimensionality reduction and classification of time embedded EEG signals. [Internet] [Masters thesis]. Colorado State University; 2007. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/28637.

Council of Science Editors:

Teli MN. Dimensionality reduction and classification of time embedded EEG signals. [Masters Thesis]. Colorado State University; 2007. Available from: http://hdl.handle.net/10217/28637

Colorado State University

23. Buehner, Michael R. Perfect tracking for non-minimum phase systems with applications to biofuels from microalgae.

Degree: PhD, Electrical and Computer Engineering, 2010, Colorado State University

URL: http://hdl.handle.net/10217/39322

► In a causal setting, a closed-loop control system receives reference inputs (with no a priori knowledge) that it must track. For this setting, controllers are… (more)

▼ In a causal setting, a closed-loop control system receives reference inputs (with no a priori knowledge) that it must track. For this setting, controllers are designed that provide both stability and performance (e.g., to meet tracking and disturbance rejection requirements). Often, feedback controllers are designed to satisfy weighted optimization criteria (e.g., weighted tracking error) that are later validated using test signals such as step responses and frequency sweeps. Feedforward controllers may be used to improve the response to measurable external disturbances (e.g., reference inputs). In this way, they can improve the closed-loop response; however, these approaches do not directly specify the closed-loop response. Two controller architectures are developed that allow for directly designing the nominal closed-loop response of non-minimum phase systems. These architectures classify both the signals that may be perfectly tracked by a non-minimum phase plant and the control signals that provide this perfect tracking. For these architectures, perfect tracking means that the feedback error is zero (for all time) in the nominal case (i.e., the plant model is exact) when there are no external disturbances. For the controllers presented here, parts of the feedforward controllers are based on the plant model, while a separate piece is designed to provide the desired level of performance. One of the potential limitations to these designs is that the actual performance will depend upon the quality of the model used. Robustness tools are developed that may be used to determine the expected performance for a given level of model uncertainty. These robustness tools may also be used to design the piece of the feedforward controller that provides performance. There is a tradeoff between model uncertainty and achievable performance. In general, more model uncertainty will result in less achievable performance. Another way to approach the issue of performance is to consider that a good model must either be known a priori or learned via adaptation. In the cases where a good model is difficult to determine a priori, adaptation may be used to improve the models in the feedforward controllers, which will, in turn, improve the performance of the overall control system. We show how adaptive feedforward architectures can improve performance for systems where the model is of limited accuracy. An example application of growing microalgae for biofuel production is presented. Microalgae have the potential to produce enough biofuels to meet the current US fuel demands; however, progress has been limited (in some part) due to a lack of appropriate models and controllers. In the work presented here, models are developed that may be used to monitor the productivity of microalgae inside a photobioreactor and to develop control algorithms. We use experimental data from a functional prototype photobioreactor to validate these models and to demonstrate the advantages of the advanced controller architectures developed here. Advisors/Committee Members: Young, Peter M. (advisor), Chong, Edwin Kah Pin (committee member), Scharf, Louis L. (committee member), Anderson, Charles W. (committee member).

Subjects/Keywords: robust; microalgae; feedforward; control systems; biofuels; adaptive; Adaptive control systems; Feedforward control systems; Biomass energy; Microalgae – Biotechnology

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APA (6^th Edition):

Buehner, M. R. (2010). Perfect tracking for non-minimum phase systems with applications to biofuels from microalgae. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/39322

Chicago Manual of Style (16^th Edition):

Buehner, Michael R. “Perfect tracking for non-minimum phase systems with applications to biofuels from microalgae.” 2010. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/39322.

MLA Handbook (7^th Edition):

Buehner, Michael R. “Perfect tracking for non-minimum phase systems with applications to biofuels from microalgae.” 2010. Web. 23 Jan 2021.

Vancouver:

Buehner MR. Perfect tracking for non-minimum phase systems with applications to biofuels from microalgae. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/39322.

Council of Science Editors:

Buehner MR. Perfect tracking for non-minimum phase systems with applications to biofuels from microalgae. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/39322

Colorado State University

24. Yadon, Carly Ann. Three types of sensory gating: exploring interrelationships, individual differences, and implications.

Degree: PhD, Psychology, 2010, Colorado State University

URL: http://hdl.handle.net/10217/39329

► The primary purpose of this dissertation was to determine how information is selectively processed in the brain through sensory gating mechanisms. Filtering, habituation, and orienting… (more)

Subjects/Keywords: Sensory receptors; Cognitive neuroscience; Neuropsychological tests; Attention – Psychological aspects

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APA (6^th Edition):

Yadon, C. A. (2010). Three types of sensory gating: exploring interrelationships, individual differences, and implications. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/39329

Chicago Manual of Style (16^th Edition):

Yadon, Carly Ann. “Three types of sensory gating: exploring interrelationships, individual differences, and implications.” 2010. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/39329.

MLA Handbook (7^th Edition):

Yadon, Carly Ann. “Three types of sensory gating: exploring interrelationships, individual differences, and implications.” 2010. Web. 23 Jan 2021.

Vancouver:

Yadon CA. Three types of sensory gating: exploring interrelationships, individual differences, and implications. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/39329.

Council of Science Editors:

Yadon CA. Three types of sensory gating: exploring interrelationships, individual differences, and implications. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/39329

Colorado State University

25. Kazeka, Alexander. Visual location awareness for mobile robots using feature-based vision.

Degree: MS(M.S.), Computer Science, 2010, Colorado State University

URL: http://hdl.handle.net/10217/38186

► This thesis presents an evaluation of feature-based visual recognition paradigm for the task of mobile robot localization. Although many works describe feature-based visual robot localization,… (more)

Subjects/Keywords: Mobile robots; Robot vision; Computer vision

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APA (6^th Edition):

Kazeka, A. (2010). Visual location awareness for mobile robots using feature-based vision. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/38186

Chicago Manual of Style (16^th Edition):

Kazeka, Alexander. “Visual location awareness for mobile robots using feature-based vision.” 2010. Masters Thesis, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/38186.

MLA Handbook (7^th Edition):

Kazeka, Alexander. “Visual location awareness for mobile robots using feature-based vision.” 2010. Web. 23 Jan 2021.

Vancouver:

Kazeka A. Visual location awareness for mobile robots using feature-based vision. [Internet] [Masters thesis]. Colorado State University; 2010. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/38186.

Council of Science Editors:

Kazeka A. Visual location awareness for mobile robots using feature-based vision. [Masters Thesis]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/38186

Colorado State University

26. Crawford-Hines, Stewart. Machine learned boundary definitions for an expert's tracing assistant in image processing.

Degree: PhD, Computer Science, 2003, Colorado State University

URL: http://hdl.handle.net/10217/28552

► Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the… (more)

▼ Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the world. In straightforward imagery this is true, however it is not always the case. There are images in which edges are indistinct or obscure, and these images can only be segmented by a human expert. The work in this dissertation addresses the range of imagery between the two extremes of those straightforward images and those requiring human guidance to appropriately segment. By freeing systems of a priori edge definitions and building in a mechanism to learn the boundary definitions needed, systems can do better and be more broadly applicable. This dissertation presents the construction of such a boundary-learning system and demonstrates the validity of this premise on real data. A framework was created for the task in which expert-provided boundary exemplars are used to create training data, which in turn are used by a neural network to learn the task and replicate the expert's boundary tracing behavior. This is the framework for the Expert's Tracing Assistant (ETA) system. For a representative set of nine structures in the Visible Human imagery, ETA was compared and contrasted to two state-of-the-art, user guided methods – Intelligent Scissors (IS) and Active Contour Models (ACM). Each method was used to define a boundary, and the distances between these boundaries and an expert's ground truth were compared. Across independent trials, there will be a natural variation in an expert's boundary tracing, and this degree of variation served as a benchmark against which these three methods were compared. For simple structural boundaries, all the methods were equivalent. However, in more difficult cases, ETA was shown to significantly better replicate the expert's boundary than either IS or ACM. In these cases, where the expert's judgement was most called into play to bound the structure, ACM and IS could not adapt to the boundary character used by the expert while ETA could. Advisors/Committee Members: Anderson, Charles W. (advisor), Draper, Bruce A. (Bruce Austin), 1962- (committee member), Beveridge, J. Ross (committee member), Alciatore, David G. (committee member).

Subjects/Keywords: visible human imagery; boundary definitions; expert's tracing assistant; ETA; intelligent scissors; IS; active contour models; ACM; boundary-learning system; Image processing; Pattern recognition systems

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APA (6^th Edition):

Crawford-Hines, S. (2003). Machine learned boundary definitions for an expert's tracing assistant in image processing. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/28552

Chicago Manual of Style (16^th Edition):

Crawford-Hines, Stewart. “Machine learned boundary definitions for an expert's tracing assistant in image processing.” 2003. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/28552.

MLA Handbook (7^th Edition):

Crawford-Hines, Stewart. “Machine learned boundary definitions for an expert's tracing assistant in image processing.” 2003. Web. 23 Jan 2021.

Vancouver:

Crawford-Hines S. Machine learned boundary definitions for an expert's tracing assistant in image processing. [Internet] [Doctoral dissertation]. Colorado State University; 2003. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/28552.

Council of Science Editors:

Crawford-Hines S. Machine learned boundary definitions for an expert's tracing assistant in image processing. [Doctoral Dissertation]. Colorado State University; 2003. Available from: http://hdl.handle.net/10217/28552

Colorado State University

27. Bush, Keith A. Echo state model of non-Markovian reinforcement learning, An.

Degree: PhD, Computer Science, 2008, Colorado State University

URL: http://hdl.handle.net/10217/28682

► There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in… (more)

▼ There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in order for reinforcement learning to be applicable. Empirical evidence, however, suggests that reinforcement learning also applies to domains where the state-action space is approximately Markovian, a requirement for the overwhelming majority of real-world domains. These domains, termed non-Markovian reinforcement learning domains, raise a unique set of practical challenges. The reconstruction dimension required to approximate a Markovian state-space is unknown a priori and can potentially be large. Further, spatial complexity of local function approximation of the reinforcement learning domain grows exponentially with the reconstruction dimension. Parameterized dynamic systems alleviate both embedding length and state-space dimensionality concerns by reconstructing an approximate Markovian state-space via a compact, recurrent representation. Yet this representation extracts a cost; modeling reinforcement learning domains via adaptive, parameterized dynamic systems is characterized by instability, slow-convergence, and high computational or spatial training complexity. The objectives of this research are to demonstrate a stable, convergent, accurate, and scalable model of non-Markovian reinforcement learning domains. These objectives are fulfilled via fixed point analysis of the dynamics underlying the reinforcement learning domain and the Echo State Network, a class of parameterized dynamic system. Understanding models of non-Markovian reinforcement learning domains requires understanding the interactions between learning domains and their models. Fixed point analysis of the Mountain Car Problem reinforcement learning domain, for both local and nonlocal function approximations, suggests a close relationship between the locality of the approximation and the number and severity of bifurcations of the fixed point structure. This research suggests the likely cause of this relationship: reinforcement learning domains exist within a dynamic feature space in which trajectories are analogous to states. The fixed point structure maps dynamic space onto state-space. This explanation suggests two testable hypotheses. Reinforcement learning is sensitive to state-space locality because states cluster as trajectories in time rather than space. Second, models using trajectory-based features should exhibit good modeling performance and few changes in fixed point structure. Analysis of performance of lookup table, feedforward neural network, and Echo State Network (ESN) on the Mountain Car Problem reinforcement learning domain confirm these hypotheses. The ESN is a large, sparse, randomly-generated, unadapted recurrent neural network, which adapts a linear projection of the target domain onto the hidden layer. ESN modeling results on reinforcement learning domains show it achieves performance comparable to lookup table and neural network architectures… Advisors/Committee Members: Anderson, Charles W. (advisor), Draper, Bruce A. (Bruce Austin), 1962- (committee member), Kirby, Michael, 1961- (committee member), Young, Peter M. (committee member).

Subjects/Keywords: reinforcement learning (machine learning); mountain car problem; reinforcement learning; Markovian; echo state network; ESN; fixed point analysis; Hybrid systems

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APA (6^th Edition):

Bush, K. A. (2008). Echo state model of non-Markovian reinforcement learning, An. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/28682

Chicago Manual of Style (16^th Edition):

Bush, Keith A. “Echo state model of non-Markovian reinforcement learning, An.” 2008. Doctoral Dissertation, Colorado State University. Accessed January 23, 2021. http://hdl.handle.net/10217/28682.

MLA Handbook (7^th Edition):

Bush, Keith A. “Echo state model of non-Markovian reinforcement learning, An.” 2008. Web. 23 Jan 2021.

Vancouver:

Bush KA. Echo state model of non-Markovian reinforcement learning, An. [Internet] [Doctoral dissertation]. Colorado State University; 2008. [cited 2021 Jan 23]. Available from: http://hdl.handle.net/10217/28682.

Council of Science Editors:

Bush KA. Echo state model of non-Markovian reinforcement learning, An. [Doctoral Dissertation]. Colorado State University; 2008. Available from: http://hdl.handle.net/10217/28682

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