ParisTech

About: ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Residual stress. The organization has 1888 authors who have published 1965 publications receiving 55532 citations. The organization is also known as: Paris Institute of Technology & ParisTech Développement.

Experimental Data and Predictions of Dissociation Conditions for Ethane and Propane Simple Hydrates in the Presence of Distilled Water and Methane, Ethane, Propane, and Carbon Dioxide Simple Hydrates in the Presence of Ethanol Aqueous Solutions

Abstract: Experimental gas hydrate dissociation data for ethane + distilled water, propane + distilled water, methane + 0.05 and 0.1 mass fraction ethanol aqueous solution, ethane + 0.05 and 0.1 mass fraction ethanol aqueous solution, propane + 0.05 and 0.1 mass fraction ethanol aqueous solution, and carbon dioxide + 0.05 and 0.1 mass fraction ethanol aqueous solution systems are reported herein. The new experimental data have been measured using an isochoric method. All the experimental data are compared with the predictions of a general correlation and a thermodynamic model. The agreements between the experimental and predicted data are generally found to be acceptable. The hydrate dissociation data for ethane + distilled water and propane + distilled water systems are also compared with some experimental data reported in the literature, and acceptable agreements between the data indicate the reliability of the experimental technique used in this study.

Complexity scalable motion compensated wavelet video encoding

Abstract: We present a framework for the systematic analysis of video encoding complexity, measured in terms of the number of motion estimation (ME) computations, that we illustrate on motion compensated wavelet video coding schemes. We demonstrate the graceful complexity scalability of these schemes through the modification of the spatiotemporal decomposition structure and the ME parameters, and the use of spatiotemporal prediction. We generate a wide range of rate-distortion-complexity (R-D-C) operating points for different sequences, by modifying these options. Using our analytical framework we derive closed form expressions for the number of ME computations for these different coding modes and show that they accurately capture the computational complexity independent of the underlying content characteristics. Our framework for complexity analysis can be combined with rate-distortion modeling to determine the encoding structure and parameters for optimal R-D-C tradeoffs.