Simulating the mobility of mobile devices has always been an important issue as far as wireless networks are concerned because mobility needs to be taken into account in various situations in wireless networks. Researchers have been trying, for many years, to improve the accuracy and flexibility of mobility models. Although recent progress of designing mobility models based on social graph have enhanced the performance of mobility models and made them more convenient to use, we believe the accuracy and flexibility of mobility models could be further improved by taking a more integrated structure as the input. In this paper, we propose a new way of designing mobility models on the basis of relational graph [1] which is a graph depicting the relation among objects, e.g. relation between people and people, and also people and places. Moreover, some novel mobility features were introduced in the proposed model to provide social, spatial and temporal properties in order to produce results similar to real mobility data. It was demonstrated by simulation that these measures could generate results similar to real mobility data.

The singular points of fingerprints, viz. core and delta, are important referential points for the classification of fingerprints. Several conventional approaches such as the Poincare index method have been proposed; however, these approaches are not reliable with poor-quality fingerprints. This paper proposes a new core and delta detection employing singular candidate analysis and an extended relational graph. Singular candidate analysis allows the use both the local and global features of ridge direction patterns and realizes high tolerance to local image noise; this involves the extraction of locations where there is high probability of the existence of a singular point. Experimental results using the fingerprint image databases FVC2000 and FVC2002, which include several poor-quality images, show that the success rate of the proposed approach is 10% higher than that of the Poincare index method for singularity detection, although the average computation time is 15%-30% greater.

A new detection methodology for both of the core and the delta of the fingerprint using the extended relational graph is presented. This paper shows the way to detect both of the core loop and the delta loop from the extended relational graph, which we proposed in order to summarize the global feature of the fingerprint ridge pattern distribution. The experimental results for 180 fingerprint samples show that the processing time is ranging from 0.34 [sec] to 0.44 [sec] for each fingerprint image by using Pentium 4 1.8 GHz Processor. In our experiments, the core and the delta were successfully extracted in 94.4% of the 180 samples.

This paper describes a new approach to detect a fingerprint core location using the extended relational graph, which is generated by the segmentation of the ridge directional image. The extended relational graph presents the adjacency between segments of the directional image and the boundary information between segments of the directional image. The boundary curves generated by the boundary information in the extended relational graph is approximated to the straight lines. The fingerprint core location is calculated as center of the gravity in the points of intersection of these approximated lines. Experimental results show that 90.8% of the 130 fingerprint samples are succeeded to detect the core location.