Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/9014
Title: Comparative analysis of graphic contents rendering techniques in a multi-view system through agent-mediator based communication
Authors: Fahad 
Dr. AZHAR Muhammad 
Sajjad, Muhammad 
Mehmood, Irfan 
Kwon, Soon II 
Lee, Jong Weon 
Baik, Sung Wook 
Issue Date: 2014
Source: Fahad, Azhar, Muhammad., Sajjad, Muhammad., Mehmood, Irfan., Kwon, Soon II., Lee, Jong-Weon. & Baik, Sung-Wook (2014). Comparative analysis of graphic contents rendering techniques in a multi-view system through agent-mediator based communication. In Jeong, Young Sik, Park, Young Ho, Hsu, Ching Hsien (Robert), Park, James J. (Jong Hyuk) (Eds.). Ubiquitous information technologies and applications. CUTE 2013, Danang, Vietnam (pp. 573-579). Springer, Berlin, Heidelberg.
Conference: The 8th International Conference on Ubiquitous Information Technologies and Applications 
Abstract: One of the major issues in mixed reality multi-agent systems is synchronization of display, which can adversely affect system performance and hinder user interaction. Real time response from the system cannot be achieved because of the aforementioned issues. If the content displayed on agents is complex and cannot be feasibly rendered on a single agent, then a better strategy is to divide the contents among multiple agents. In this way, only a fraction of the entire contents is rendered by each agent. In this paper, two alternative techniques for multi-agent based content management are proposed, namely, full contents on agents (FCOA) and partial contents on agents (PCOA). In FCOA, each agent in the multi-agent system renders all the contents and only a specific part of the contents is displayed depending upon the usage scenario. In PCOA the agents receive partial contents from the mediator. A comparative study has been presented in this paper to identify the pros and cons of each method
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/9014
ISBN: 9783642416705
9783642416712
DOI: https://doi.org/10.1007/978-3-642-41671-2_73
Appears in Collections:Applied Data Science - Publication

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