Non-ideal behaviour of a room temperature ionic liquid in an alkoxyethanol or poly ethers at T = (298.15 to 318.15) K

Non-ideal behaviour of 1-butyl-3-methylimidazolium hexafluorophosphate [bmim][PF6] in ethylene glycol monomethyl ether; CH3OCH2CH2OH (EGMME), ethylene glycol dimethyl ether; CH3OCH2CH2OCH3 (EGDME) and diethylene glycol dimethyl ether; CH3(OCH2CH2)2OCH3 (DEGDME) have been investigated over the whole composition range at T = (298.15 to 318.15) K. To gain insight into the mixing behaviour, results of density measurements were used to estimate excess molar volumes, image, apparent molar volumes, Vphi,i, partial molar volumes, image, excess partial molar volumes, image, and their limiting values at infinite dilution, image, image, and image, respectively. Volumetric results have been analyzed in the light of Prigogine–Flory–Patterson (PFP) statistical mechanical theory. Measurements of refractive indices n were also performed for all the binary mixtures over whole composition range at T = 298.15 K. Deviations in refractive indices ?phin and the deviation of molar refraction ?xR have been calculated from experimental data. Refractive indices results have been correlated with volumetric results and have been interpreted in terms of molecular interactions. Excess properties are fitted to the Redlich–Kister polynomial equation to obtain the binary coefficients and the standard errors.

The outflow ranking method for weighted directed graphs

A ranking method assigns to every weighted directed graph a (weak) ordering of the nodes. In this paper we axiomatize the ranking method that ranks the nodes according to their outflow using four independent axioms. Besides the well-known axioms of anonymity and positive responsiveness we introduce outflow monotonicity – meaning that in pairwise comparison between two nodes, a node is not doing worse in case its own outflow does not decrease and the other node’s outflow does not increase – and order preservation – meaning that adding two weighted digraphs such that the pairwise ranking between two nodes is the same in both weighted digraphs, then this is also their pairwise ranking in the ‘sum’ weighted digraph. The outflow ranking method generalizes the ranking by outdegree for directed graphs, and therefore also generalizes the ranking by Copeland score for tournaments.

SoC memory hierarchy derivation from dataflow graphs

Hardware synthesis from dataflow graphs of signal processing systems is a growing research area as focus shifts to high level design methodologies. For data intensive systems, dataflow based synthesis can lead to an inefficient usage of memory due to the restrictive nature of synchronous dataflow and its inability to easily model data reuse. This paper explores how dataflow graph changes can be used to drive both the on-chip and off-chip memory organisation and how these memory architectures can be mapped to a hardware implementation. By exploiting the data reuse inherent to many image processing algorithms and by creating memory hierarchies, off-chip memory bandwidth can be reduced by a factor of a thousand from the original dataflow graph level specification of a motion estimation algorithm, with a minimal increase in memory size. This analysis is verified using results gathered from implementation of the motion estimation algorithm on a Xilinx Virtex-4 FPGA, where the delay between the memories and processing elements drops from 14.2 ns down to 1.878 ns through the refinement of the memory architecture. Care must be taken when modeling these algorithms however, as inefficiencies in these models can be easily translated into overuse of hardware resources.

A similarity measure for graphs with low computational complexity

We present and analyze an algorithm to measure the structural similarity of generalized trees, a new graph class which includes rooted trees. For this, we represent structural properties of graphs as strings and define the similarity of two Graphs as optimal alignments of the corresponding property stings. We prove that the obtained graph similarity measures are so called Backward similarity measures. From this we find that the time complexity of our algorithm is polynomial and, hence, significantly better than the time complexity of classical graph similarity methods based on isomorphic relations. (c) 2006 Elsevier Inc. All rights reserved.