Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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- Creators: Electrical Engineering Program
Description
This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.
ContributorsKarpur, Ajay (Author) / Suzuki, Kotoka (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
Penetration testing is regarded as the gold-standard for understanding how well an organization can withstand sophisticated cyber-attacks. However, the recent prevalence of markets specializing in zero-day exploits on the darknet make exploits widely available to potential attackers. The cost associated with these sophisticated kits generally precludes penetration testers from simply obtaining such exploits – so an alternative approach is needed to understand what exploits an attacker will most likely purchase and how to defend against them. In this paper, we introduce a data-driven security game framework to model an attacker and provide policy recommendations to the defender. In addition to providing a formal framework and algorithms to develop strategies, we present experimental results from applying our framework, for various system configurations, on real-world exploit market data actively mined from the darknet.
ContributorsRobertson, John James (Author) / Shakarian, Paulo (Thesis director) / Doupe, Adam (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In modern remote sensing, arrays of sensors, such as antennas in radio frequency (RF) systems and microphones in acoustic systems, provide a basis for estimating the direction of arrival of a narrow-band signal at the sensor array. A Uniform linear array (ULA) is the most well-studied array geometry in that its performance characteristics and limitations are well known, especially for signals originating in the far field. In some instances, the geometry of an array may be perturbed by an environmental disturbance that actually changes its nominal geometry; such as, towing an array behind a moving vehicle. Additionally, sparse arrays have become of interest again due to recent work in co-prime arrays. These sparse arrays contain fewer elements than a ULA but maintain the array length. The effects of these alterations to a ULA are of interest. Given this motivation, theoretical and experimental (i.e. via computer simulation) processes are used to determine quantitative and qualitative effects of perturbation and sparsification on standard metrics of array performance. These metrics include: main lobe gain, main lobe width and main lobe to side lobe ratio. Furthermore, in order to ascertain results/conclusions, these effects are juxtaposed with the performance of a ULA. Through the perturbation of each element following the first element drawn from a uniform distribution centered around the nominal position, it was found that both the theoretical mean and sample mean are relatively similar to the beam pattern of the full array. Meanwhile, by using a sparsification method of maintaining all the lags, it was found that this particular method was unnecessary. Simply taking out any three elements while maintaining the length of the array will produce similar results. Some configurations of elements give a better performance based on the metrics of interest in comparison to the ULA. These results demonstrate that a sparsified, perturbed or sparsified and perturbed array can be used in place of a Uniform Linear Array depending on the application.
ContributorsSilbernagel, Drake Oliver (Author) / Cochran, Douglas (Thesis director) / Aberle, James (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This document introduces the need for the Rest Egg system and defines an accessible method of smartphone integration. Excessive noise can prevent recovering patients and special needs persons from resting correctly. The Rest Egg was designed for these people- people who are in critical need of quality rest but are often unable to eliminate stressors themselves. This system ensures their environment is calm by alerting caretakers' smartphones if noise reaches abrasive levels. Smartphones were the preferred device due to the wide spread of such devices in today's market. After making open sourcing a goal, something ubiquitous and affordable \u2014 yet usable and dependable \u2014 was necessary for the alert system. These requirements lead to the election an online alert service: Pushover, a trademark and product of Superblock, LLC.
ContributorsJennings, Tyler Blake (Author) / Goryll, Michael (Thesis director) / Kozicki, Michael (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2016-05
Description
RRAM is an emerging technology that looks to replace FLASH NOR and possibly NAND memory. It is attractive because it uses an adjustable resistance and does not rely on charge; in the sub-10nm feature size circuitry this is critical. However, RRAM cross-point arrays suffer tremendously from leakage currents that prevent proper readings in larger array sizes. In this research an exponential IV selector was added to each cell to minimize this current. Using this technique the largest array-size supportable was determined to be 512x512 cells using the conventional voltage sense amplifier by HSPICE simulations. However, with the increase in array size, the sensing latency also remarkably increases due to more sneak path currents, approaching 873 ns for the 512x512 array.
ContributorsMadler, Ryan Anton (Author) / Yu, Shimeng (Thesis director) / Cao, Yu (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The artificial neural network is a form of machine learning that is highly effective at recognizing patterns in large, noise-filled datasets. Possessing these attributes uniquely qualifies the neural network as a mathematical basis for adaptability in personal biomedical devices. The purpose of this study was to determine the viability of neural networks in predicting Freezing of Gait (FoG), a symptom of Parkinson's disease in which the patient's legs are suddenly rendered unable to move. More specifically, a class of neural networks known as layered recurrent networks (LRNs) was applied to an open- source FoG experimental dataset donated to the Machine Learning Repository of the University of California at Irvine. The independent variables in this experiment \u2014 the subject being tested, neural network architecture, and sampling of the majority classes \u2014 were each varied and compared against the performance of the neural network in predicting future FoG events. It was determined that single-layered recurrent networks are a viable method of predicting FoG events given the volume of the training data available, though results varied significantly between different patients. For the three patients tested, shank acceleration data was used to train networks with peak precision/recall values of 41.88%/47.12%, 89.05%/29.60%, and 57.19%/27.39% respectively. These values were obtained for networks optimized using detection theory rather than optimized for desired values of precision and recall. Furthermore, due to the nature of the experiments performed in this study, these values are representative of the lower-bound performance of layered recurrent networks trained to detect gait freezing. As such, these values may be improved through a variety of measures.
ContributorsZia, Jonathan Sargon (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Adler, Charles (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
A distributed sensor network (DSN) is a set of spatially scattered intelligent sensors designed to obtain data across an environment. DSNs are becoming a standard architecture for collecting data over a large area. We need registration of nodal data across the network in order to properly exploit having multiple sensors. One major problem worth investigating is ensuring the integrity of the data received, such as time synchronization. Consider a group of match filter sensors. Each sensor is collecting the same data, and comparing the data collected to a known signal. In an ideal world, each sensor would be able to collect the data without offsets or noise in the system. Two models can be followed from this. First, each sensor could make a decision on its own, and then the decisions could be collected at a ``fusion center'' which could then decide if the signal is present or not. The fusion center can then decide if the signal is present or not based on the number true-or-false decisions that each sensor has made. Alternatively, each sensor could relay the data that it collects to the fusion center, and it could then make a decision based on all of the data that it then receives. Since the fusion center would have more information to base its decision on in the latter case--as opposed to the former case where it only receives a true or false from each sensor--one would expect the latter model to perform better. In fact, this would be the gold standard for detection across a DSN. However, there is random noise in the world that causes corruption of data collection, especially among sensors in a DSN. Each sensor does not collect the data in the exact same way or with the same precision. We classify these imperfections in data collections as offsets, specifically the offset present in the data collected by one sensor with respect to the rest of the sensors in the network. Therefore, reconsider the two models for a DSN described above. We can naively implement either of these models for data collection. Alternatively, we can attempt to estimate the offsets between the sensors and compensate. One could see how it would be expected that estimating the offsets within the DSN would provide better overall results than not finding estimators. This thesis will be structured as follows. First, there will be an extensive investigation into detection theory and the impact that different types of offsets have on sensor networks. Following the theory, an algorithm for estimating the data offsets will be proposed correct for the offsets. Next, we will look at Monte Carlo simulation results to see the impact on sensor performance of data offsets in comparison to a sensor network without offsets present. The algorithm is then implemented, and further experiments will demonstrate sensor performance with offset detection.
ContributorsMonardo, Vincent James (Author) / Cochran, Douglas (Thesis director) / Kierstead, Hal (Committee member) / Electrical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper analyzes existing literature regarding how excessive aggravating stimuli in a hospital environment can reduce the quality and quantity of sleep. The sick and injured are most sensitive to aggravating stimuli and the most vulnerable to poor sleep conditions. For individuals with anxiety, stress, hypersensitivity, or conditions such as Autism Spectrum Disorder (ASD), as additional stress during rest periods could seriously harm development and overall well-being. While solutions have been proposed and tested, there is no one solution to the problem. One possible solution is to design a device that monitors a patient's room and alerts a nurse or parent of aggravating stimuli so that it can be removed.
ContributorsKhan, Zarah Noor (Author) / Goryll, Michael (Thesis director) / Adams, James B. (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Built-In Self-Test for Simultaneous Transmit and Receive (BIST for STAR) will be able to solve the challenges of transmitting and receiving at the same time at the same frequency. One of the major components is the STAR antenna which transmits and receives along the same pathway. The main problem with doing both on the same path is that the transmit signal is usually much stronger in power compared to the received signal. The transmit signal has echoes and leakages that cause self-interference, preventing the received signal from being properly obtained. The solution developed in this project is the BIST component, which will help calculate the functional gain and phase offset of the interference signal and subtract it from the pathway so that the received signal remains. The functions of the proposed circuit board can be modeled in Matlab, where an emulation code generates a random, realistic functional gain and delay for the interference. From the generated values, the BIST for STAR was simulated to output what the measurements would be given the strength of the input signal and a controlled delay. The original Matlab code models an ideal environment directly recalculating the functional gain and phase from the given measurements in a second Matlab script. The actual product will not be ideal; a possible source of error to be considered is the effect of thermal noise. To observe the effect of noise on the BIST for STAR's performance, the Matlab code was expanded upon to include a component for thermal noise, and a method of analyzing the results of the board.
ContributorsLiu, Jennifer Yuan (Author) / Ozev, Sule (Thesis director) / Kozicki, Michael (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05