Virtual Reality (VR) technology has been used widely today in Science, Technology, Engineering and Mathematics (STEM) fields. The VR is emerging computer interface distinguished by high degrees of immersion, trustworthy, and interaction. The goal of VR is making the user believe, as much as possible, that he is within the computergenerated environment. The VR has become one of the important technologies to be discussed regarding its applications, usage, and its different types that can achieve huge benefits in the real world. This survey paper introduces detail information about VR systems and requirements to build correct VR environment. Moreover, this work presents a comparison between system types of VR. Then, it presents the tools and software used for building VR environments. After that, we epitomize a road of the map for selecting appropriate VR system according to the field of applications. Finally, we introduce the conclusion and future predictions to develop the VR systems.

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The demand for safety-boosting systems is always increasing, especially to limit the rapid spread of COVID-19. Real-time social distance preserving is an essential application towards containing the pandemic outbreak. Few systems have been proposed which require infrastructure setup and high-end phones. Therefore, they have limited ubiquitous adoption. Cellular technology enjoys widespread availability and their support by commodity cellphones which suggest leveraging it for social distance tracking. However, users sharing the same environment may be connected to different teleco providers of different network configurations. Traditional cellular-based localization systems usually build a separate model for each provider, leading to a drop in social distance performance. In this paper, we propose CellTrace, a deep learning-based social distance preserving system. Specifically, CellTrace finds a cross-provider representation using a deep learning version of Canonical Correlation Analysis. Different providers' data are highly correlated in this representation and used to train a localization model for estimating the social distances. Additionally, CellTrace incorporates different modules that improve the deep model's generalization against overtraining and noise. We have implemented and evaluated CellTrace in two different environments with a side-by-side comparison with the state-of-theart cellular localization and contact tracing techniques. The results show that CellTrace can accurately localize users and estimate the contact occurrence, regardless of the connected providers, with a sub-meter median error and 97% accuracy, respectively. In addition, we show that CellTrace has robust performance in various challenging scenarios.