Theses and Dissertations
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Browsing Theses and Dissertations by Author "Anderson, Charles, committee member"
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Item Open Access Accurate dimension reduction based polynomial chaos approach for uncertainty quantification of high speed networks(Colorado State University. Libraries, 2018) Krishna Prasad, Aditi, author; Roy, Sourajeey, advisor; Pezeshki, Ali, committee member; Notaros, Branislav, committee member; Anderson, Charles, committee memberWith the continued miniaturization of VLSI technology to sub-45 nm levels, uncertainty in nanoscale manufacturing processes and operating conditions have been found to translate into unpredictable system-level behavior of integrated circuits. As a result, there is a need for contemporary circuit simulation tools/solvers to model the forward propagation of device level uncertainty to the network response. Recently, techniques based on the robust generalized polynomial chaos (PC) theory have been reported for the uncertainty quantification of high-speed circuit, electromagnetic, and electronic packaging problems. The major bottleneck in all PC approaches is that the computational effort required to generate the metamodel scales in a polynomial fashion with the number of random input dimensions. In order to mitigate this poor scalability of conventional PC approaches, in this dissertation, a reduced dimensional PC approach is proposed. This PC approach is based on using a high dimensional model representation (HDMR) to quantify the relative impact of each dimension on the variance of the network response. The reduced dimensional PC approach is further extended to problems with mixed aleatory and epistemic uncertainties. In this mixed PC approach, a parameterized formulation of analysis of variance (ANOVA) is used to identify the statistically significant dimensions and subsequently perform dimension reduction. Mixed problems are however characterized by far greater number of dimensions than purely epistemic or aleatory problems, thus exacerbating the poor scalability of PC expansions. To address this issue, in this dissertation, a novel dimension fusion approach is proposed. This approach fuses the epistemic and aleatory dimensions within the same model parameter into a mixed dimension. The accuracy and efficiency of the proposed approaches are validated through multiple numerical examples.Item Open Access Control system design for plasma power generator(Colorado State University. Libraries, 2022) Sankaran, Aishwarya, author; Young, Peter M., advisor; Chong, Edwin, committee member; Anderson, Charles, committee memberThe purpose of this research is to develop advanced control strategies for precise control over power delivery to nonlinear plasma loads at high frequency. A high-fidelity MATLAB/Simulink simulation model was provided by Advanced Energy Industries, Inc (AE) and the data from this model was considered as the actual model under consideration. The research work requires computing a mathematical model of the plasma power generator system, analyzing and synthesizing robust controllers for individual operating points, and then developing a control system that covers the entire the grid of operating points. The modeling process involves developing computationally simple near-linear models representing relevant frequencies and operating points for the system consisting of nonlinear plasma load, RF Power Amplifier, and a Match Network. To characterize the (steady-state) mapping from power setpoint to delivered power the steady-state gains of the system are taken under consideration. Linear and nonlinear system identification procedures are used to adequately capture both the nonlinear steady-state gains and the linear dynamic model response. These near-linear or linear models with uncertainty description to characterize the robustness requirements are utilized in the second stage to develop a grid of robust controller designed at linear operating points. The controller from -synthesis design process optimizes robust performance for allowable perturbations as large as possible. It does all this while guaranteeing closed-loop stability for all allowable perturbations. The final stage of the research focuses on developing Linear Parameter Varying (LPV) controllers with non-linear offset. This single controller covers the entire operating range, including the case that the desired signals to track may vary over wide regions of the operating envelope. LPV controllers allows actual power to track the changing setpoint in a smooth manner over the entire operating range.Item Open Access Machine learning techniques for energy optimization in mobile embedded systems(Colorado State University. Libraries, 2012) Donohoo, Brad Kyoshi, author; Pasricha, Sudeep, advisor; Anderson, Charles, committee member; Jayasumana, Anura P., committee memberMobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for communication, business, and social interactions. While performance, capabilities, and design are all important considerations when purchasing a mobile device, a long battery lifetime is one of the most desirable attributes. Battery technology and capacity has improved over the years, but it still cannot keep pace with the power consumption demands of today's mobile devices. This key limiter has led to a strong research emphasis on extending battery lifetime by minimizing energy consumption, primarily using software optimizations. This thesis presents two strategies that attempt to optimize mobile device energy consumption with negligible impact on user perception and quality of service (QoS). The first strategy proposes an application and user interaction aware middleware framework that takes advantage of user idle time between interaction events of the foreground application to optimize CPU and screen backlight energy consumption. The framework dynamically classifies mobile device applications based on their received interaction patterns, then invokes a number of different power management algorithms to adjust processor frequency and screen backlight levels accordingly. The second strategy proposes the usage of machine learning techniques to learn a user's mobile device usage pattern pertaining to spatiotemporal and device contexts, and then predict energy-optimal data and location interface configurations. By learning where and when a mobile device user uses certain power-hungry interfaces (3G, WiFi, and GPS), the techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression, and k-nearest neighbor, are able to dynamically turn off unnecessary interfaces at runtime in order to save energy.Item Open Access Novel methods to quantify aleatory and epistemic uncertainty in high speed networks(Colorado State University. Libraries, 2017) Kapse, Ishan Deepak, author; Roy, Sourajeet, advisor; Pasricha, Sudeep, committee member; Anderson, Charles, committee memberWith the sustained miniaturization of integrated circuits to sub-45 nm regime and the increasing packaging density, random process variations have been found to result in unpredictability in circuit performance. In existing literature, this unpredictability has been modeled by creating polynomial expansions of random variables. But the existing methods prove inefficient because as the number of random variables within a system increase, the time and computational cost increases in a near-polynomial fashion. In order to mitigate this poor scalability of conventional approaches, several techniques are presented, in this dissertation, to sparsify the polynomial expansion. The sparser polynomial expansion is created, by identifying the contribution of each random variable on the total response of the system. This sparsification is performed primarily using two different methods. It translates to immense savings, in the time required, and the memory cost of computing the expansion. One of the two methods presented is applied to aleatory variability problems while the second method is applied to problems involving epistemic uncertainty. The accuracy of the proposed approaches is validated through multiple numerical examples.Item Open Access Smart indoor localization using machine learning techniques(Colorado State University. Libraries, 2014) Ugave, Viney Anand, author; Pasricha, Sudeep, advisor; Anderson, Charles, committee member; Roy, Sourajeet, committee memberThe advancement of smartphone devices has led to a generation of new applications and solutions. These devices give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user's context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization whereas interest in location-based services is also rising. While numerous smartphone based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. More importantly, these techniques entail high-energy consumption that can quickly drain a smartphone battery. In this thesis, we propose innovative techniques based on machine learning algorithms and smart sensor management for effective Indoor Localization using smartphones. We evaluated our techniques on several indoor environments with diverse characteristics and show improvements over several state-of-the-art techniques from prior work. The extensive use of sensors and Wi-Fi scans can deplete the smartphone battery and so we quantitatively accounted for all the modules that consume the battery power. We also performed energy and accuracy tradeoff analysis to provide a broader understanding of how to smartly use these techniques. Furthermore, we investigated, implemented and tested both sensor and machine learning based techniques. Our best technique achieved an average accuracy between 1-3 meters across most of our evaluated indoor paths.Item Open Access Unattended acoustic sensor systems for noise monitoring in national parks(Colorado State University. Libraries, 2017) Yaremenko, Vladimir, author; Azimi-Sadjadi, Mahmood R., advisor; Pezeshki, Ali, committee member; Anderson, Charles, committee memberDetection and classification of transient acoustic signals is a difficult problem. The problem is often complicated by factors such as the variety of sources that may be encountered, the presence of strong interference and substantial variations in the acoustic environment. Furthermore, for most applications of transient detection and classification, such as speech recognition and environmental monitoring, online detection and classification of these transient events is required. This is even more crucial for applications such as environmental monitoring as it is often done at remote locations where it is unfeasible to set up a large, general-purpose processing system. Instead, some type of custom-designed system is needed which is power efficient yet able to run the necessary signal processing algorithms in near real-time. In this thesis, we describe a custom-designed environmental monitoring system (EMS) which was specifically designed for monitoring air traffic and other sources of interest in national parks. More specifically, this thesis focuses on the capabilities of the EMS and how transient detection, classification and tracking are implemented on it. The Sparse Coefficient State Tracking (SCST) transient detection and classification algorithm was implemented on the EMS board in order to detect and classify transient events. This algorithm was chosen because it was designed for this particular application and was shown to have superior performance compared to other algorithms commonly used for transient detection and classification. The SCST algorithm was implemented on an Artix 7 FPGA with parts of the algorithm running as dedicated custom logic and other parts running sequentially on a soft-core processor. In this thesis, the partitioning and pipelining of this algorithm is explained. Each of the partitions was tested independently to very their functionality with respect to the overall system. Furthermore, the entire SCST algorithm was tested in the field on actual acoustic data and the performance of this implementation was evaluated using receiver operator characteristic (ROC) curves and confusion matrices. In this test the FPGA implementation of SCST was able to achieve acceptable source detection and classification results despite a difficult data set and limited training data. The tracking of acoustic sources is done through successive direction of arrival (DOA) angle estimation using a wideband extension of the Capon beamforming algorithm. This algorithm was also implemented on the EMS in order to provide real-time DOA estimates for the detected sources. This algorithm was partitioned into several stages with some stages implemented in custom logic while others were implemented as software running on the soft-core processor. Just as with SCST, each partition of this beamforming algorithm was verified independently and then a full system test was conducted to evaluate whether it would be able to track an airborne source. For the full system test, a model airplane was flown at various trajectories relative to the EMS and the trajectories estimated by the system were compared to the ground truth. Although in this test the accuracy of the DOA estimates could not be evaluated, it was show that the algorithm was able to approximately form the general trajectory of a moving source which is sufficient for our application as only a general heading of the acoustic sources is desired.