Feasibility of Neural Networks for Maritime Visual Detection on a Mobile Platform

Object detection through computer vision has traditionally been difficult to reliably implement due to various lighting conditions caused by weather and time of day. Any changes in conditions can be detrimental to the detector’s ability to accurately identify objects. A modern approach implements deep learning techniques to classify and train a neural network. While highly effective, this approach can be cumbersome and computationally intensive. This project will investigate the feasibility of using deep learning to detect, classify, and track objects in near real-time while being processed on a mobile platform. I will investigate the feasibility of these processes on a small embedded system, such as the NVIDIA Jetson TX1. I will investigate several promising algorithms such as Faster R-CNN, TensorBox, DetectNet, and YOLO. This research is beneficial because it will transition deep learning techniques developed primarily for research in a lab environment to a real-world situation in which high accuracy and fast processing are vital. The work solved through this research will greatly benefit platforms that require object detection capabilities, but do not have the space, budget, or power capabilities for large GPUs or GPU clusters

Communicative competence: A cross-cultural study of first conversations between strangers

Thesis (M.A.)--University of Kansas, Communication Studies, 1986

A False Sense of Security: Due Process Failures in Removal Proceedings

The article explores the reasons for the failure of due process rights afforded by aliens facing criminal prosecution for unauthorized return to the U.S. after prior removal proceedings. Topics discussed include Federal enforcement of the Immigration and Nationality Act, laws governing criminal prosecution and incarceration for previously removed aliens, and disclosure of the availability of judicial review to aliens facing removal from the U.S

Forgotten But Not Forgiven: Remedies for Student Loan Debtors in Public Service

This Note identifies a timely issue with student loans and discusses potential remedies. Under the Public Service Loan Forgiveness program, borrowers of federal student loans who make 120 qualifying monthly payments while working in public service may have their loans forgiven. When a loan is forgiven, the borrower no longer has to pay the remaining balance of the debt. Since both the prevalence of student loans and the average student debt are increasing, loan forgiveness is an important opportunity for borrowers under the crushing debt of student loans. However, a Consumer Financial Protection Bureau report found a rising problem: Loan servicers are leading borrowers to believe that borrowers’ monthly payments qualify for loan forgiveness, when they in fact do not. Right now, the only solution for those borrowers is to restart the clock on their ten years of public service. This Note determines that there are several causes of action that could be brought against student loan servicers in order to offer relief to borrowers who believed their loans would be forgiven