Bulletin of the American Physical Society
2018 Joint Fall Meeting of the Texas Sections of APS, AAPT and Zone 13 of the SPS
Volume 63, Number 18
Friday–Saturday, October 19–20, 2018; University of Houston, Houston, Texas
Session P01: SPS - Undergraduate (or High School) Research II |
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Chair: Andrew Renshaw, University of Houston Room: Science and Engineering Classroom (SEC) 103 |
Saturday, October 20, 2018 2:10PM - 2:22PM |
P01.00001: Designing an End-To-End Interface for Optics Experiments Megan Drown, Calvin Berggren Within the optics lab at Texas Lutheran University, certain light intensity sensors were previously built and used, and the sensors were connected to an Arduino microcontroller. However, these sensors were difficult to utilize without an easy way to interface to the computer. The goal of this research was to design a Jupyter-based interface to use these sensors in a simple and efficient manner. The interface developed automatically handles interfacing to the computer, setting the gain on the sensor, integrating over time, reporting uncertainty, accounting for the background, and more. |
Saturday, October 20, 2018 2:22PM - 2:34PM |
P01.00002: Data Compression and Machine Learning for Quantum States Debora Mroczek, Eric R Bittner, Vladimir N Lankevich As an attempt to simplify the analysis of electron-hole dynamics, we make a case for the use of the Shannon Entropy as measure of the degree of entanglement of electron-hole pairs using Schmidt decomposition. The Schmidt form also allows us to determine the number of effective dimensions occupied by an arbitrary eigenstate in Hilbert space. In the case of a 50x50 diabatic density matrix for a 1-D system, we were able to reduce the number of bytes required to store this information by approximately 65% using the Schmidt form. We then reconstructed the original matrix from the reduced model and calculated values for charge transfer character and inverse participation ratio and found that the average percent error was less than 0.05% in both cases. Lastly, we demonstrate that machine learning algorithms can be used to accurately differentiate between excitonic, charge-transfer, and charge-separated states. By combining data compression and machine learning, we developed a simplified and computationally efficient way to quickly sift through thousands of eigenstates and single out relevant information about the nature of the system. |
Saturday, October 20, 2018 2:34PM - 2:46PM |
P01.00003: Building Blocks for Machine Learning in Medical Imaging Dylan J Martinez, Amar Kavuri, William Nisbett, Mini Das Our prior research has shown a correlation between several second order image texture features and human observer studies in tomographic breast images. Digital breast tomosynthesis (DBT) acquisition parameters were tested for both sensitivity and specificity performance for low contrast mass detection via localization receiver operating characteristic (LROC) studies. These acquisition parameters dictate the optimal imaging dose vs. signal detectability for an imaging geometry or reconstruction algorithm being tested. Simulated phantom images were generated using different acquisition parameters as part of the virtual clinical trial platform being developed in our laboratory. These correlations between the image texture features and human attention can aid in efficient system designs capable of image classification and localization of signals such as calcification clusters and low contrast cancer. In this study, we explore image features linked to texture parameters in order to characterize the human observer’s regions of interest (ROIs). Our findings have the potential to unveil essential features and building blocks for a fast and efficient machine learning algorithm in tomographic breast imaging. |
Saturday, October 20, 2018 2:46PM - 2:58PM |
P01.00004: Theoretical Modeling of Photoluminescence from InGaAs/GaAs Single Strained Quantum Well Dallas G Slusser, Toni D Sauncy Quantum wells formed with III-V semiconductors are used in a variety of optoelectronic device applications. In order to better understand the emission characteristics of the quantum well system, it is important to have a detailed model that accounts for all the physical parameters that have an effect on electronic transitions in the system. The goal of this work was to employ a simple root-finding function in python to calculate the photoluminescence (PL) emission for an InxGa1-xAs/GaAs strained quantum well heterostructure, with the ultimate goal of introducing the temperature dependence in several parameters to model the PL as a function of temperature. A code has been developed that allowed for varying model parameters to better understand the influence of each parameter on the transition energy. The model compares well with experimental data, which deviates from well-known empirical relationships. |
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