WEHST: Wearable Engine for Human-Mediated Telepresence

This dissertation reports on the industrial design of a wearable computational device created to enable better emergency medical intervention for situations where electronic remote assistance is necessary. The design created for this doctoral project, which assists practices by paramedics with mandates for search-and-rescue (SAR) in hazardous environments, contributes to the field of human-mediated teleparamedicine (HMTPM). Ethnographic and industrial design aspects of this research considered the intricate relationships at play in search-and-rescue operations, which lead to the design of the system created for this project known as WEHST: Wearable Engine for Human-Mediated Telepresence. Three case studies of different teams were carried out, each focusing on making improvements to the practices of teams of paramedics and search-and-rescue technicians who use combinations of ambulance, airplane, and helicopter transport in specific chemical, biological, radioactive, nuclear and explosive (CBRNE) scenarios. The three paramedicine groups included are the Canadian Air Force 442 Rescue Squadron, Nelson Search and Rescue, and the British Columbia Ambulance Service Infant Transport Team. Data was gathered over a seven-year period through a variety of methods including observation, interviews, examination of documents, and industrial design. The data collected included physiological, social, technical, and ecological information about the rescuers. Actor-network theory guided the research design, data analysis, and design synthesis. All of this leads to the creation of the WEHST system. As identified, the WEHST design created in this dissertation project addresses the difficulty case-study participants found in using their radios in hazardous settings. As the research identified, a means of controlling these radios without depending on hands, voice, or speech would greatly improve communication, as would wearing sensors and other computing resources better linking operators, radios, and environments. WEHST responds to this need. WEHST is an instance of industrial design for a wearable “engine” for human-situated telepresence that includes eight interoperable families of wearable electronic modules and accompanying textiles. These make up a platform technology for modular, scalable and adaptable toolsets for field practice, pedagogy, or research. This document details the considerations that went into the creation of the WEHST design

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Greening the Maple

Ecocriticism can be described in very general terms as the investigation of the many ways in which culture and the environment are interrelated and conceptualized. Ecocriticism aspires to understand and often to celebrate the natural world, yet it does so indirectly by focusing primarily on written texts. Hailed as one of the most timely and provocative developments in literary and cultural studies of recent decades, it has also been greeted with bewilderment or scepticism by those for whom its aims and methods are unclear. This book seeks to bring into view the development of ecocriticism in the context of Canadian literary studies. Selections include work by Margaret Atwood, Northrop Frye, Sherrill Grace, and Rosemary Sullivan