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Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected. In this paper we present an image based steganography that combines Least Significant Bit(LSB), Discrete Cosine Transform(DCT), and compression techniques on raw images to enhance the security of the payload. Initially, the LSB algorithm is used to embed the payload bits into the cover image to derive the stego-image. The stego-image is transformed from spatial domain to the frequency domain using DCT. Finally quantization and runlength coding algorithms are used for compressing the stego-image to enhance its security. It is observed that secure images with low MSE and BER are transferred without using any password, in comparison with earlier works.

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a SelfAdaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

Wavelength routed optical networks have emerged as a technology that can eectively utilize the enormous bandwidth of the optical ®ber. Wavelength converters play an important role in enhancing the ®ber utilization and reducing the overall call blocking probability of the network. As the distortion of the optical signal increases with the increase in the range of wavelength conversion in optical wavelength converters, limited range wavelength conversion assumes importance. Placement of wavelength converters is a NP complete problem [K.C. Lee, V.O.K. Li, IEEE J. Lightwave Technol. 11 (1993) 962±970] in an arbitrary mesh network. In this paper, we investigate heuristics for placing limited range wavelength converters in arbitrary mesh wavelength routed optical networks. The objective is to achieve near optimal placement of limited range wavelength converters resulting in reduced blocking probabilities and low distortion of the optical signal. The proposed heuristic is to place limited range wavelength converters at the most congested nodes, nodes which lie on the long lightpaths and nodes where conversion of optical signals is signi®cantly high. We observe that limited range converters at few nodes can provide almost the entire improvement in the blocking probability as the full range wavelength converters placed at all the nodes. Congestion control in the network is brought about by dynamically adjusting the weights of the channels in the link thereby balancing the load and reducing the average delay of the trac in the entire network. Simulations have been carried out on a 12-node ring network, 14-node NSFNET, 19-node European Optical Network (EON), 28-node US long haul network, hypothetical 30-node INET network and the results agree with the analysis. Ó

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