The paper presents the highest 5644 PPI Micro-display completion which performs the FHD resolution in one 0.39 inch display. A 4.5um pixel by sub-pixel rendering (SPR) technic is applied to achieve the demonstration in reliable and qualified image performance for near-eye application especially in the field of virtual reality (VR)and augmented reality (AR) .
PurposeThis paper presents a reclassification of markers for mixed reality environments that is also applicable to the use of markers in robot navigation systems and 3D modelling. In the case of Augmented Reality (AR) mixed reality environments, markers are used to integrate computer generated (virtual) objects into a predominantly real world, while in Augmented Virtuality (AV) mixed reality environments, the goal is to integrate real objects into a predominantly virtual (computer generated) world. Apart from AR/AV classifications, mixed reality environments have also been classified by reality; output technology/display devices; immersiveness as well as by visibility of markers.Design/methodology/approachThe approach adopted consists of presenting six existing classifications of mixed reality environments and then extending them to define new categories of abstract, blended, virtual augmented, active and smart markers. This is supported with results/examples taken from the joint Mixed Augmented and Virtual Reality Laboratory (MAVRLAB) of the Ulster University, Belfast, Northern Ireland; the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy and Santasco SrL, Regio Emilia/Milan, Italy.FindingsExisting classification of markers and mixed reality environments are mainly binary in nature and do not adequately capture the contextual relationship between markers and their use and application. The reclassification of markers into abstract, blended and virtual categories captures the context for simple use and applications while the categories of augmented, active and smart markers captures the relationship for enhanced or more complex use of markers. The new classifications are capable of improving the definitions of existing simple marker and markerless mixed reality environments as well as supporting more complex features within mixed reality environments such as co-location of objects, advanced interactivity, personalised user experience.Research limitations/implicationsIt is thought that applications and devices in mixed reality environments when properly developed and deployed enhances the real environment by making invisible information visible to the user. The current work only marginally covers the use of internet of things (IoT) devices in mixed reality environments as well as potential implications for robot navigation systems and 3D modelling.Practical implicationsThe use of these reclassifications enables researchers, developers and users of mixed reality environments to select and make informed decisions on best tools and environment for their respective application, while conveying information with additional clarity and accuracy. The development and application of more complex markers would contribute in no small measure to attaining greater advancements in extending current knowledge and developing applications to positively impact entertainment, business and health while minimizing costs and maximizing benefits.Originality/valueThe originality of this paper lies in the approach adopted in reclassifying markers. This is supported with results and work carried out at the MAV Reality Laboratory of Ulster University, Belfast–UK, the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste-Italy and Santasco SrL, Regio Emilia, Milan–Italy. The value of present research lies in the definitions of new categories as well as the discussions of how they improve mixed reality environments and application especially in the health and education sectors.
Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. Offloading tasks to the fog that is closer to IoT users for processing has become a means to ensure that tasks are completed quickly. Fog computing can not only reduce the congestion of the backbone network but also ensure that the task is completed within the specified time. Since fog resources are limited, there will be resource competition among IoT devices. How to quickly and efficiently make an optimal computation offloading decision for individual selfish IoT devices is a fundamental research issue. This article regards the process of multiple IoT devices competing for fog devices as a game and proposes a distributed computation offloading algorithm. The goal is to optimize the balance of computation delay, energy consumption, and cost for fog nodes. The competition between IoT nodes eventually reaches an equilibrium point, that is the Nash equilibrium point. We prove the existence of Nash equilibrium by Weighted Potential Game. In addition, if a large number of IoT devices select the same node for offloading, which will cause the fog node to run out of power and make some networks unable to work normally. Further, causing part of the network to be paralyzed. Therefore, the paper considers the fairness of offloading to extend the network life cycle. A calculation rate adjustment algorithm is designed for the fairness of offloading to ensure that fog nodes do not run out of power and fail. This paper not only fully considers the performance of the IoT device, but also considers the fairness of the fog. Numerous experiments proved the effectiveness of the proposed algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.