Matching Items (247)
Description
Current technology does not allow for the full amount of power produced by solar arrays (PV) on spacecraft to be utilized. The arrays are designed with non-reconfigurable architectures and sent on fifteen to twenty year long missions. They cannot be changed once they are in space, so the arrays are designed for the end of life. Throughout their lifetime, solar arrays can degrade in power producing capabilities anywhere from 20% to 50%. Because there is such a drastic difference in the beginning and end of life power production, and because they cannot be reconfigured, a new design has been found necessary in order to increase power production. Reconfiguration allows the solar arrays to achieve maximum power producing capabilities at both the beginning and end of their lives. With the potential to increase power production by 50%, the reconfiguration design consists of a switching network to be able to utilize any combination of cells. The design for reconfiguration must meet the power requirements of the solar array. This thesis will explore different designs for reconfiguration, as well as possible switches for implementation. It will also review other methods to increase power production, as well as discuss future work in this field.
ContributorsJohnson, Everett Hope (Author) / Kitchen, Jennifer (Thesis director) / Ozev, Sule (Committee member) / School of International Letters and Cultures (Contributor) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Detecting early signs of neurodegeneration is vital for measuring the efficacy of pharmaceuticals and planning treatments for neurological diseases. This is especially true for Amyotrophic Lateral Sclerosis (ALS) where differences in symptom onset can be indicative of the prognosis. Because it can be measured noninvasively, changes in speech production have been proposed as a promising indicator of neurological decline. However, speech changes are typically measured subjectively by a clinician. These perceptual ratings can vary widely between clinicians and within the same clinician on different patient visits, making clinical ratings less sensitive to subtle early indicators. In this paper, we propose an algorithm for the objective measurement of flutter, a quasi-sinusoidal modulation of fundamental frequency that manifests in the speech of some ALS patients. The algorithm detailed in this paper employs long-term average spectral analysis on the residual F0 track of a sustained phonation to detect the presence of flutter and is robust to longitudinal drifts in F0. The algorithm is evaluated on a longitudinal speech dataset of ALS patients at varying stages in their prognosis. Benchmarking with two stages of perceptual ratings provided by an expert speech pathologist indicate that the algorithm follows perceptual ratings with moderate accuracy and can objectively detect flutter in instances where the variability of the perceptual rating causes uncertainty.
ContributorsPeplinski, Jacob Scott (Author) / Berisha, Visar (Thesis director) / Liss, Julie (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Speech nasality disorders are characterized by abnormal resonance in the nasal cavity. Hypernasal speech is of particular interest, characterized by an inability to prevent improper nasalization of vowels, and poor articulation of plosive and fricative consonants, and can lead to negative communicative and social consequences. It can be associated with a range of conditions, including cleft lip or palate, velopharyngeal dysfunction (a physical or neurological defective closure of the soft palate that regulates resonance between the oral and nasal cavity), dysarthria, or hearing impairment, and can also be an early indicator of developing neurological disorders such as ALS. Hypernasality is typically scored perceptually by a Speech Language Pathologist (SLP). Misdiagnosis could lead to inadequate treatment plans and poor treatment outcomes for a patient. Also, for some applications, particularly screening for early neurological disorders, the use of an SLP is not practical. Hence this work demonstrates a data-driven approach to objective assessment of hypernasality, through the use of Goodness of Pronunciation features. These features capture the overall precision of articulation of speaker on a phoneme-by-phoneme basis, allowing demonstrated models to achieve a Pearson correlation coefficient of 0.88 on low-nasality speakers, the population of most interest for this sort of technique. These results are comparable to milestone methods in this domain.
ContributorsSaxon, Michael Stephen (Author) / Berisha, Visar (Thesis director) / McDaniel, Troy (Committee member) / Electrical Engineering Program (Contributor, Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Previous studies have shown that experimentally implemented formant perturbations result in production of compensatory responses in the opposite direction of the perturbations. In this study, we investigated how participants adapt to a) auditory perturbations that shift formants to a specific point in the vowel space and hence remove variability of formants (focused perturbations), and b) auditory perturbations that preserve the natural variability of formants (uniform perturbations). We examined whether the degree of adaptation to focused perturbations was different from adaptation to uniform adaptations. We found that adaptation magnitude of the first formant (F1) was smaller in response to focused perturbations. However, F1 adaptation was initially moved in the same direction as the perturbation, and after several trials the F1 adaptation changed its course toward the opposite direction of the perturbation. We also found that adaptation of the second formant (F2) was smaller in response to focused perturbations than F2 responses to uniform perturbations. Overall, these results suggest that formant variability is an important component of speech, and that our central nervous system takes into account such variability to produce more accurate speech output.
ContributorsDittman, Jonathan William (Author) / Daliri, Ayoub (Thesis director) / Berisha, Visar (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The Phoenix CubeSat is a 3U Earth imaging CubeSat which will take infrared (IR) photos of cities in the United Stated to study the Urban Heat Island Effect, (UHI) from low earth orbit (LEO). It has many different components that need to be powered during the life of its mission. The only power source during the mission will be its solar panels. It is difficult to calculate power generation from solar panels by hand because of the different orientations the satellite will be positioned in during orbit; therefore, simulation will be used to produce power generation data. Knowing how much power is generated is integral to balancing the power budget, confirming whether there is enough power for all the components, and knowing whether there will be enough power in the batteries during eclipse. This data will be used to create an optimal design for the Phoenix CubeSat to accomplish its mission.
ContributorsBarakat, Raymond John (Author) / White, Daniel (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Buck converters are a class of switched-mode power converters often used to step down DC input voltages to a lower DC output voltage. These converters naturally produce a current and voltage ripple at their output due to their switching action. Traditional methods of reducing this ripple have involved adding large discrete inductors and capacitors to filter the ripple, but large discrete components cannot be integrated onto chips. As an alternative to using passive filtering components, this project investigates the use of active ripple cancellation to reduce the peak output ripple. Hysteretic controlled buck converters were chosen for their simplicity of design and fast transient response. The proposed cancellation circuits sense the output ripple of the buck converter and inject an equal ripple exactly out of phase with the sensed ripple. Both current-mode and voltage-mode feedback loops are simulated, and the effectiveness of each cancellation circuit is examined. Results show that integrated active ripple cancellation circuits offer a promising substitute for large discrete filters.
ContributorsWang, Ziyan (Author) / Bakkaloglu, Bertan (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This paper reviews several current designs of Cube Satellite (CubeSat) Electrical Power Systems (EPS) based on Silicon FET technologies and their current deficiencies, such as radiation-incurred defects and switching power losses. A strategy to fix these is proposed by the way of using Gallium Nitride (GaN) High Electron-Mobility Transistors (HEMTs) as switching devices within Buck/Boost Converters and other regulators. This work summarizes the EPS designs of several CubeSat missions, classifies them, and outlines their efficiency. An in-depth example of an EPS is also given, explaining the process in which these systems are designed. Areas of deficiency are explained along with reasoning as to why GaN can mitigate these losses, including its wide bandgap properties such as high RDS(on) and High Breakdown Voltage. Special design considerations must be kept in mind when using GaN HEMTs in this application and an example of a CubeSat using GaN HEMTs is mentioned. Finally, challenges ahead for GaN are explored including manufacturing considerations and long-term reliability.
ContributorsWilloughby, Alexander George (Author) / Kitchen, Jennifer (Thesis director) / Zhao, Yuji (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
In competitive Taekwondo, Electronic Body Protectors (EBPs) are used to register hits made by players during sparring. EBPs are comprised of three main components: chest guard, foot sock, and headgear. This equipment interacts with each other through the use of magnets, electric sensors, transmitters, and a receiver. The receiver is connected to a computer programmed with software to process signals from the transmitter and determine whether or not a competitor scored a point. The current design of EBPs, however, have numerous shortcomings, including sensing false positives, failing to register hits, costing too much, and relying on human judgment. This thesis will thoroughly delineate the operation of the current EBPs used and discuss research performed in order to eliminate these weaknesses.
ContributorsSpell, Valerie Anne (Author) / Kozicki, Michael (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Divergence functions are both highly useful and fundamental to many areas in information theory and machine learning, but require either parametric approaches or prior knowledge of labels on the full data set. This paper presents a method to estimate the divergence between two data sets in the absence of fully labeled data. This semi-labeled case is common in many domains where labeling data by hand is expensive or time-consuming, or wherever large data sets are present. The theory derived in this paper is demonstrated on a simulated example, and then applied to a feature selection and classification problem from pathological speech analysis.
ContributorsGilton, Davis Leland (Author) / Berisha, Visar (Thesis director) / Cochran, Douglas (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This work details the bootstrap estimation of a nonparametric information divergence measure, the Dp divergence measure, using a power law model. To address the challenge posed by computing accurate divergence estimates given finite size data, the bootstrap approach is used in conjunction with a power law curve to calculate an asymptotic value of the divergence estimator. Monte Carlo estimates of Dp are found for increasing values of sample size, and a power law fit is used to relate the divergence estimates as a function of sample size. The fit is also used to generate a confidence interval for the estimate to characterize the quality of the estimate. We compare the performance of this method with the other estimation methods. The calculated divergence is applied to the binary classification problem. Using the inherent relation between divergence measures and classification error rate, an analysis of the Bayes error rate of several data sets is conducted using the asymptotic divergence estimate.
ContributorsKadambi, Pradyumna Sanjay (Author) / Berisha, Visar (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05