5 research outputs found
Assessing usability of full-body immersion in an interactive virtual reality environment
No full text2020 Summer.Includes bibliographical references.Improving immersion and playability has a direct impact on the effectiveness of certain Virtual Reality applications. This project looks at understanding how to develop an immersive soccer application with the intention to measure skills, particularly for the use of assessment and health promotion. This project will show the requirements to create a top-down immersive experience with commodity devices. The particular system serves the simulation of a soccer training environment to evade opponents, pass to teammates, and score goals with the objective of measuring the difficulty of single, double, and triple tasks. It is expected that the performance will go down as the level of tasks increases. This hypothesis is extremely relevant as it provides a system that could serve as an assessment tool for people with concussions to return to play (with an OK by a physician) or to promote exercise to non-athletes. This thesis provides all the necessary steps to explain the high-level details of highly immersive applications while providing a future-path for human-subject experiments
CubeVR: Digital Affordances for Architecture Undergraduate Education using Virtual Reality
No full textLong-Term and Short-Term Traffic Forecasting Using Holt-Winters Method
No full textThe need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.</jats:p
Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method
No full textThe need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.</jats:p
