Comparison of a Virtual Game-Day Experience on Varying Devices

Collegiate athletics, particularly football, provide tremendous value to schools through branding, revenue, and publicity. As a result, extensive effort is put into recruiting talented students. When recruiting, home games are exceptional tools used to show a school\u27s unique game-day atmosphere. However, this is not a viable option during the offseason or for off-site visits. This paper explores a solution to these challenges by using virtual reality (VR) to recreate the game-day experience. The Virtual Reality Application Center in conjunction with Iowa State University (ISU) athletics, created a VR application mimicking the game-day experience at ISU. This application was displayed using the world\u27s highest resolution six-sided CAVETM, an Oculus Rift DK2 computer-driven head mounted display (HMD) and a Merge VR smart phone-driven HMD. A between-subjects user study compared presence between the different systems and a video control. In total, 82 students participated, indicating their presence using the Witmer and Singer questionnaire. Results revealed that while the CAVETM scored the highest in presence, the Oculus and Merge only experienced a slight drop compared to the CAVETM. This result suggests that the mobile ultra-low-cost Merge is a viable alternative to the CAVE TM and Oculus for delivering the game-day experience to ISU recruits

A computation study on contextual self-organizing maps for subset data integration

As sensing and data collection capabilities have dramatically increased in recent years, many areas from medicine to entertainment to engineering have to rethink how products are designed, delivered and maintained. In engineering fields data is everywhere and its use as a decision aid, in the constant stream of tradeoff decisions, is critical to delivering more robust products and services accurately and efficiently. Thus, the need to develop intelligent methods to analyze and visualize large datasets, to enable human understanding, is critical. One method that has been proven effective in this endeavor is the self-organizing map (SOM). However, SOMs require substantial computational resources and time to train, making them impractical for large datasets or datasets that may be added to over time. If this issue could be overcome, this approach could be widely adopted. This thesis studies the concept of using a subset of data to represent the characteristics of a full data set via a SOM. The correlation of a subset and full dataset SOM was studied on two different test cases. The percent difference of node weights was used to compare map representations between the partial and full datasets. A node alignment process was designed and implemented to enable a more accurate comparison of two SOMs. The methodology was evaluated on two test cases. A hundred comparisons of node weights from subset and full datasets maps were completed per test case. Results showed that pairing node weights by row and column designation did not accurately compare two different SOMs. The alignment process was then performed on ten samples of map comparisons per test case. Results of the aligned nodes provided a much more accurate comparison of SOMs from partial and full datasets. The results of this study show that with a good representative subset of data very similar nodal weights can be reached through map training compared to using the full dataset. This allows a trained SOM to be available as a decision aid in a fraction of the training time compared to using the full dataset.</p

A computation study on contextual self-organizing maps for subset data integration

As sensing and data collection capabilities have dramatically increased in recent years, many areas from medicine to entertainment to engineering have to rethink how products are designed, delivered and maintained. In engineering fields data is everywhere and its use as a decision aid, in the constant stream of tradeoff decisions, is critical to delivering more robust products and services accurately and efficiently. Thus, the need to develop intelligent methods to analyze and visualize large datasets, to enable human understanding, is critical. One method that has been proven effective in this endeavor is the self-organizing map (SOM). However, SOMs require substantial computational resources and time to train, making them impractical for large datasets or datasets that may be added to over time. If this issue could be overcome, this approach could be widely adopted. This thesis studies the concept of using a subset of data to represent the characteristics of a full data set via a SOM. The correlation of a subset and full dataset SOM was studied on two different test cases. The percent difference of node weights was used to compare map representations between the partial and full datasets. A node alignment process was designed and implemented to enable a more accurate comparison of two SOMs. The methodology was evaluated on two test cases. A hundred comparisons of node weights from subset and full datasets maps were completed per test case. Results showed that pairing node weights by row and column designation did not accurately compare two different SOMs. The alignment process was then performed on ten samples of map comparisons per test case. Results of the aligned nodes provided a much more accurate comparison of SOMs from partial and full datasets. The results of this study show that with a good representative subset of data very similar nodal weights can be reached through map training compared to using the full dataset. This allows a trained SOM to be available as a decision aid in a fraction of the training time compared to using the full dataset

Best practices for cross-platform virtual reality development

Virtual Reality (VR) simulations have become a major component of the US Military and commercial training. VR is an attractive training method because it is readily available at a lower cost than traditional training methods. This has led to a staggering increase in demand for VR technology and research. To meet this demand, game engines such as Unity3D and Unreal have made substantial efforts to support various forms of VR, including the HTC Vive, smartphone-enabled devices like the GearVR, and with appropriate plugins, even fully-immersive Cave Automatic Virtual Environment (CAVETM) systems. Because of this hardware diversity, there is a need to develop VR applications that can operate on several systems, also known as cross-platform development. The goal in developing applications for all these types of systems is to create a consistent user experience across the devices. It is challenging to maintain this consistent user experience, because many VR devices have fundamental differences. Research has begun to explore ways of developing one application for multiple system. The Virtual Reality Applications Center (VRAC) developed a VR football “Game Day” simulation that was deployed to three devices: CAVETM, Oculus Rift HMD and a mobile HMD. Development of this application presented many learning opportunities regarding cross-platform development. There is no single approach to achieving consistency across VR systems, but the authors hope to disseminate these best practices in cross-platform VR development through the game day application example. This research will help the US Military develop applications to be deployed across many VR systems.This proceeding is published as Jonathan Schlueter, Holly Baiotto, Melynda Hoover, Vijay Kalivarapu, Gabriel Evans, Eliot Winer, "Best practices for cross-platform virtual reality development," Proc. SPIE 10197, Degraded Environments: Sensing, Processing, and Display 2017, 1019709 (5 May 2017); doi: 10.1117/12.2262718. Posted with permission.</p

Comparison of a Virtual Game-Day Experience on Varying Devices

Collegiate athletics, particularly football, provide tremendous value to schools through branding, revenue, and publicity. As a result, extensive effort is put into recruiting talented students. When recruiting, home games are exceptional tools used to show a school's unique game-day atmosphere. However, this is not a viable option during the offseason or for off-site visits. This paper explores a solution to these challenges by using virtual reality (VR) to recreate the game-day experience. The Virtual Reality Application Center in conjunction with Iowa State University (ISU) athletics, created a VR application mimicking the game-day experience at ISU. This application was displayed using the world's highest resolution six-sided CAVETM, an Oculus Rift DK2 computer-driven head mounted display (HMD) and a Merge VR smart phone-driven HMD. A between-subjects user study compared presence between the different systems and a video control. In total, 82 students participated, indicating their presence using the Witmer and Singer questionnaire. Results revealed that while the CAVETM scored the highest in presence, the Oculus and Merge only experienced a slight drop compared to the CAVETM. This result suggests that the mobile ultra-low-cost Merge is a viable alternative to the CAVE TM and Oculus for delivering the game-day experience to ISU recruits.This article is published as Miller, Jack, Holly Baiotto, Anastacia MacAllister, Melynda Hoover, Gabriel Evans, Jonathan Schlueter, Vijay Kalivarapu, and Eliot Winer. "Comparison of a Virtual Game-Day Experience on Varying Devices." Electronic Imaging 2017, no. 16 (2017): 30-37. DOI: 10.2352/ISSN.2470-1173.2017.16.CVAS-346. Posted with permission.</p