Prisons, Genres, and Big Data: Understanding the Language of Corrections in America\u27s Prisons

This dissertation seeks to answer one fundamental question: How can I as a researcher conduct social justice research that is ethical, durable, and portable? As social justice research becomes more prominent in the field of technical and professional communication, ethical research practices must be maintained to avoid an unintentional wounding of the subjects for whom researchers hope to advocate. The dissertation is divided into five sections, each written as a stand-alone article that builds on the principles of the section before it. Each section addresses a key question: 1) How do I ethically engage in social justice research? 2) How do I ethically engage with big data and algorithmic rhetorics? 3) How do I frame my research to have the most impact outside my home discipline? 4) What does an ethical, computational content analysis look like? 5) How do these principles translate into the classroom? Together, these articles identify a methodology called Institutional Genre Analysis, which focuses on text as data that was produced by an institution rather than individual users, avoiding many of the pitfalls of big data research while providing a means for what Vitanza calls “intellectual guerilla warfare conducted by [marginalized individuals]” (1987, p. 52)

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT