Multi-constraint quality-of-service routing will become increasingly important as the Internet evolves to support real-time services. It is well known however, that optimum multi-constraint QoS routing is computationally complex, and for this reason various heuristics have been proposed for routing in practical situations. Among these methods, those that use a single mixed metric are the most popular. Although mixed metric routing discards potentially useful information, this is compensated for by significantly reduced complexity. Exploiting this tradeoff is becoming increasingly important where low complexity designs are desired, such as in battery operated wireless applications. In this paper, a novel single mixed metric multi-constraint routing algorithm is introduced. The proposed technique has similar complexity compared with existing low complexity methods. Simulation results are presented which show that it can obtain better performance than comparable techniques in terms of generating feasible multi-constraint QoS routes.

In this paper a steganalysis method is proposed for pixel value differencing method. This steganographic method, which has been immune against conventional attacks, performs the embedding in the difference of the values of pixel pairs. Therefore, the histogram of the differences of an embedded image is different as compared with a cover image. A number of characteristics are identified in the difference histogram that show meaningful alterations when an image is embedded. Five distinct multilayer perceptrons neural networks are trained to detect different levels of embedding. Every image is fed in to all networks and a voting system categorizes the image as stego or cover. The implementation results indicate an 88.6 % success in correct categorization of the test images. c ○ 2009 ISC. All rights reserved. 1

Abstract: In this paper a new method for determining the search area for motion estimation algorithm based on block matching is suggested. In the proposed method the search area is adaptively found for each block of a frame. This search area is similar to that of the full search (FS) algorithm but smaller for most blocks of a frame. Therefore, the proposed algorithm is analogous to FS in terms of regularity but has much less computational complexity. To find the search area, the temporal and spatial correlations among the motion vectors of blocks are used. Based on this, the matched block is chosen from a rectangular area that the prediction vectors set out. Simulation results indicate that the speed of the proposed algorithm is at least 7 times better than the FS algorithm.

Program at a Glance................................................ 4