AXMEDIS project (Automating Production of Cross Media Content for Multi-channel Distribution) is partially funded by the European Commission to create an innovative technology framework for the automatic production, protection and distribution of digital cross-media contents over a range of different media channels including PC (on the internet), PDA, kiosk, mobile phones and i-TV (interactive-TV). The AXMEDIS project has proposed a set of integrated solutions and technologies that covers data model and DRM. This paper presents a brief introduction to the AXMEDIS 1ST FP6 EC project, while discussing the new functionalities enabled by the AXMEDIS architecture and solution in terms of interoperable content and DRM among different distribution channels. For further details on the AXMEDIS project, see the project website at www.axmedis.org.
A large part of models for digital rights management include methods for logging the information related to the actions performed by final users on the content and on the players. These actions can be associated with the exploitation of the rights granted in the license, and may include other activities such as eventual changes of preference, installations, certifications, etc. In this area, relevant models have been defined by MPEG-21, whose usage and some extensions are reported in this paper. This paper discusses the new functionalities enabled by the AXMEDIS architecture and solution in terms of interoperable content and DRM among different distribution channels in relationship with the tracking and accounting of actions performed on the content by the final users on the players and on authoring tools. AXMEDIS aims at creating an innovative technology framework for the automatic production, protection and distribution of digital cross-media contents over a range of different media channels, including PC (on the internet), PDA, kiosks, mobile phones and i-TV (interactive-TV). For further details on the AXMEDIS project, see the project website at www.axmedis.org.
This paper analyzes a method to approximate the first passage time probability density function which turns to be particularly useful if only sample data are available. The method relies on a Laguerre-Gamma polynomial approximation and iteratively looks for the best degree of the polynomial such that the fitting function is a probability density function. The proposed iterative algorithm relies on simple and new recursion formulae involving first passage time moments. These moments can be computed recursively from cumulants, if they are known. In such a case, the approximated density can be used also for the maximum likelihood estimates of the parameters of the underlying stochastic process. If cumulants are not known, suitable unbiased estimators relying on κ-statistics are employed. To check the feasibility of the method both in fitting the density and in estimating the parameters, the first passage time problem of a geometric Brownian motion is considered.
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