Recent advancements in machine learning methods have allowed companies to develop advanced computer vision aided production lines that take advantage of the raw and labeled data captured by high-definition cameras mounted at vantage points in their factory floor. We experiment with two different methods of developing one such system to automatically track key components on a production line. By tracking the state of these key components using object detection we can accurately determine and report production line metrics like part arrival and start/stop times for key factory processes. We began by collecting and labeling raw image data from the cameras overlooking the factory floor. Using that data we trained two dedicated object detection models. Our training utilized transfer learning to start from a Faster R-CNN ResNet model trained on Microsoft’s COCO dataset. The first model we developed is a binary classifier that detects the state of a single object while the second model is a multiclass classifier that detects the state of two distinct objects on the factory floor. Both models achieved over 95% classification and localization accuracy on our test datasets. Having two additional classes did not affect the classification or localization accuracy of the multiclass model compared to the binary model.

Th NTRU cryptosystem is a lattice-based encryption scheme. Several parameters determine the speed, size, correctness rate and security of the algorithm. These parameters need to be carefully selected for the algorithm to function correctly. This thesis includes a short overview of the NTRU algorithm and its mathematical background before discussing the results of experimentally testing various different parameter sets for NTRU and determining the effect that different relationships between these parameters have on the overall effectiveness of NTRU.

The purpose of this thesis is to accurately simulate in 3D the HH901 jet in the Mystic Mountain Formation of the Carina Nebula. Astronomers present a narrow-band Wide Field Camera image of Carina and the morphology of some astrophysical jets, including HH901. The simulation attempts to replicate features of the jet, among which are pulses, bow shock, terminal Mach disk, and Kelvin-Helmholtz rollup. We use the gas dynamical equations to solve for density, velocity, and temperature. The numerical methods used to solve the equations are third-order WENO (weighted essentially non-oscillatory) and third-order Runge-Kutta. Graphs of density and radiative cooling demonstrate the effect of adding wind (nonzero ambient velocity). The paper discusses the altering of the ambient velocity and final time to fit the shape of the jet in the Hubble image. The suggested next steps are simulating the other HH901 jet and comparing the jets’ atomic makeups to advance understanding of astrophysical jets.

Applying a classical theorem due to Macbeath applied to a suitably sized horoball, we calculate novel group presentations for singly-cusped Bianchi groups. We find new presentations for Bianchi groups with d = -43, -67, -163. With previously known presentations for d = -1, -2, -3, -7, -11, -19, this constitutes a complete set of presentations for singly-cusped Bianchi groups.

This paper addresses echo chambers, an online phenomena wherein social media users can "only hear their own voice". In this paper I will examine the history and recent proliferation of online echo chambers. I will outline a comprehensive theory of echo chamber generation and maintenance, intended for educational value. I then conduct my own experiment based on previous echo chamber detection work.

Molecular pathology makes use of estimates of tumor content (tumor percentage) for pre-analytic and analytic purposes, such as molecular oncology testing, massive parallel sequencing, or next-generation sequencing (NGS), assessment of sample acceptability, accurate quantitation of variants, assessment of copy number changes (among other applications), determination of specimen viability for testing (since many assays require a minimum tumor content to report variants at the limit of detection) may all be improved with more accurate and reproducible estimates of tumor content. Currently, tumor percentages of samples submitted for molecular testing are estimated by visual examination of Hematoxylin and Eosin (H&E) stained tissue slides under the microscope by pathologists. These estimations can be automated, expedited, and rendered more accurate by applying machine learning methods on digital whole slide images (WSI).