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- Creators: Mechanical and Aerospace Engineering Program
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Description
Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. Independent parameters provide a means to trade-off code tracking discriminant gain against multipath mitigation performance. The algorithm performance is characterized in terms of multipath phase error bias, phase error estimation variance, tracking range, tracking ambiguity and implementation complexity. The algorithm is suitable for modernized GNSS signals including Binary Phase Shift Keyed (BPSK) and a variety of Binary Offset Keyed (BOC) signals. The algorithm compensates for unbalanced code sequences to ensure a code tracking bias does not result from the use of asymmetric correlation kernels. The algorithm does not require explicit knowledge of the propagation channel model. Design recommendations for selecting the algorithm parameters to mitigate precorrelation filter distortion are also provided.
ContributorsMiller, Steven (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
ContributorsBraun, Henry (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
ContributorsLee, Jongmin (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Microbial fuel cells(MFC) use micro-organisms called anode-respiring bacteria(ARB) to convert chemical energy into electrical energy. This process can not only treat wastewater but can also produce useful byproduct hydrogen peroxide(H2O2). Process variables like anode potential and pH play important role in the MFC operation and the focus of this dissertation are pH and potential control problems.
Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation.
A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance.
Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat.
Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation.
A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance.
Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat.
ContributorsJoshi, Rakesh (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Torres, Cesar (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Over the past several years, there has been growing concern regarding concussions and traumatic brain injuries (TBIs) in all levels of sports. A concussion is a traumatic brain injury that occurs from a blow to the head. When a concussion occurs, the brain knocks against the walls of the skull. A concussion causes temporary loss of brain function leading to cognitive, physical, and emotional symptoms, such as confusion, vomiting,headache, nausea,depression, disturbed sleep, moodiness, and amnesia. Although the short-term effects of concussions are limited, the long-term effects of concussions, if untreated, can be devastating and even life-threatening. Concussions are having detrimental ramifications on society and it is important to know what these ramifications are. Concussions are a common occurrence in traditional physical sports such as soccer, basketball, and football. However, due to the violent nature of football (American football), concussions are more prevalent and the effects are more severe. Changes to rules and equipment, specifically helmets, have been made to reduce head impacts in football but there is not currently enough evidence to conclude that they significantly lessen the frequency and severity of concussions.
ContributorsLaughlin, Riley James (Author) / Squires, Kyle (Thesis director) / Shrake, Scott (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
The flipped classroom is a teaching method that flips the activities done in and out of class, i.e., concepts are learned out of class and problems are worked in class under the supervision of the instructor. Studies have indicated several benefits of the FC, including improved performance and engagement. In the past years, further studies have investigated the benefits of FC in statics, dynamics, and mechanics of materials courses and indicate similar performance benefits. However, these studies address a need for additional studies to validate their results due to the short length of their research or small classroom size. In addition, many of these studies do not measure student attitudes, such as self-efficacy, or the difference in time spent out of class on coursework. The objective of this research is to determine the effectiveness of the flipped classroom system (FC) in comparison to the traditional classroom system (TC) in a large mechanics of materials course. Specifically, it aims to measure student performance, student self-efficacy, student attitudes on lecture quality, motivation, attendance, hours spent out of class, practice, and support, and difference in impact between high, middle, and low achieving students. In order to accomplish this, three undergraduate mechanics of materials courses were analyzed during the spring 2015 semester. One FC section served as the experimental group (92 students), while the two TC sections served as the control group (125 students). To analyze student self-efficacy and attitudes, a survey instrument was designed to measure 18 variables and was administered at the end of the semester. Standardized core outcomes were compared between groups to analyze performance. This paper presents the specific course framework used in this FC, detailed results of the quantitative and qualitative analysis, and discussion of strengths and weaknesses. Overall, an overwhelming majority of students were satisfied with FC and would like more of their classes taught using FC. Strengths of this teaching method include greater confidence, better focus, higher satisfaction with practice in class and assistance received from instructors and peers, more freedom to express ideas and questions in class, and less time required outside of class for coursework. Results also suggest that this method has a greater positive impact on high and low achieving students and leads to higher performance. The criticisms made by students focused on lecture videos to have more worked examples. Overall, results suggest that FC is more effective than TC in a large mechanics of materials course.
ContributorsLee, Andrew Ryan (Author) / Zhu, Haolin (Thesis director) / Middleton, James (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
A robotic exploration mission that would enter a lunar pit to characterize the environment is described. A hopping mechanism for the robot's mobility is proposed. Various methods of hopping drawn from research literature are discussed in detail. The feasibilities of mechanical, electric, fluid, and combustive methods are analyzed. Computer simulations show the mitigation of the risk of complex autonomous navigation systems. A mechanical hopping mechanism is designed to hop in Earth gravity and carry a payload half its mass. A physical experiment is completed and proves a need for further refinement of the prototype design. Future work is suggested to continue exploring hopping as a mobility method for the lunar robot.
ContributorsMcKinney, Tyler James (Author) / Thangavelautham, Jekan (Thesis director) / Robinson, Mark (Committee member) / Asphaug, Erik (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
This thesis investigates the viability of a solar still for desalination of a personal water supply. The end goal of the project is to create a design that meets the output requirement while tailoring the components to focus on low cost so it would be feasible in the impoverished areas of the world. The primary requirement is an output of 3 liters of potable water per day, the minimum necessary for an adult human. The study examines the effect of several design parameters, such as the basin material, basin thickness, starting water depth, basin dimensions, cover material, cover angle, and cover thickness. A model for the performance of a solar still was created in MATLAB to simulate the system's behavior and sensitivity to these parameters. An instrumented prototype solar still demonstrated viability of the concept and provided data for validation of the MATLAB model.
ContributorsRasmussen, Dylan James (Author) / Wells, Valana (Thesis director) / Trimble, Steven (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2015-05
Description
The exhaust system is an integral part of any internal combustion engine. A well- designed exhaust system efficiently removes exhaust gasses expelled from the cylinders. If tuned for performance purposes, the exhaust system can also exhibit scavenging and supercharging characteristics. This project reviews the major components of an exhaust system and discusses the proper design techniques necessary to utilize the performance boosting potential of a tuned exhaust system for a four-stroke engine. These design considerations are then applied to Arizona State University's Formula SAE vehicle by comparing the existing system to a properly tuned system. An inexpensive testing method, developed specifically for this project, is used to test the effectiveness of the current design. The results of the test determined that the current design is ineffective at scavenging neighboring pipes of exhaust gasses and should be redesigned for better performance.
ContributorsKnutsen, Jeffrey Scott (Author) / Huang, Huei-Ping (Thesis director) / Steele, Bruce (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05