Research Article 
Modeling Multimedia Synchronization using Petri Nets
Information Technology Journal, 2009, 8(7), 1054-1058.
Abstract
In this study, Logical Time Interaction Petri Nets (LTIPN) were designed to describe multimedia synchronization based on the previous models. In the model, we introduce logical expressions which are used to describe passing value indeterminacy in an logical time Petri net to model multimedia synchronization. And all multimedia synchronization events including multimedia objects are expressed by transitions of Petri nets, while the previous models mostly use places of Petri nets to express multimedia objects. This study provides users simple and intuitive modeling approaches. Basic temporal relations between multimedia objects, multimedia synchronization strategies and user interactive operations can be represented simply and explicitly by the LTIPN.
ASCI-ID: 28-860
Similar Articles
Petri Net Methods of Constructing Kleene-Closure Operations of Regular Languages
Information Technology Journal, 2008, 7(4), 689-693.
Modeling Non-Repudiation in Distributed SystemsInformation Technology Journal, 2008, 7(1), 228-230.
Soundness Analysis of T-Restricted Interorganizational Logical Workflow NetsInformation Technology Journal, 2009, 8(6), 821-829.
An Approach of Data Mining Process Based on Stochastic Well-formed WorkflowsInformation Technology Journal, 2014, 13(13), 2224-2228.
A Compositional Petri Nets Semantics for Basic LotosInformation Technology Journal, 2007, 6(1), 110-116.
Cited By
A vector matching method for analysing logic Petri nets
Enterprise Information Systems, 2011, (), 1. DOI: 10.1080/17517575.2010.541943
Architecture of Dynamic Service Composition Using Logic Petri NetsJournal of Software Engineering and Applications, 2011, 04(10), 585. DOI: 10.4236/jsea.2011.410068
The Ant Colony Algorithm Based on Logic Time Petri Nets and Application in Electronic-Commerce LogisticsIEEE Access, 2019, 7(), 169011. DOI: 10.1109/ACCESS.2019.2954986
Synchronous Colored Petri Net Based Modeling and Video Analysis of Conversational Head-Gestures for Training Social RobotsLecture Notes in Networks and Systems, 2022, 359(), 476. DOI: 10.1007/978-3-030-89880-9_36