Lalit M. Patnaik

A Secure Image Steganography using LSB, DCT and Compression Techniques on Raw Images

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.

A self-adaptive migration model genetic algorithm for data mining applications

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 Self-Adaptive 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.

Epileptic EEG detection using neural networks and post-classification

Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet coefficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained.

Self-tuning minimum-variance control of nonlinear systems of the Hammerstein model

Self-tuning is applied to the control of nonlinear systems represented by the Hammerstein model wherein the nonlinearity is any odd-order polynomial. But control costing is not feasible in general. Initial relay control is employed to contain the deviations.

Secure Authentication using Image Processing and Visual Cryptography for Banking Applications

Core banking is a set of services provided by a group of networked bank branches. Bank customers may access their funds and perform other simple transactions from any of the member branch offices. The major issue in core banking is the authenticity of the customer. Due to unavoidable hacking of the databases on the Internet, it is always quite difficult to trust the information on the Internet. To solve this problem of authentication, we are proposing an algorithm based on image processing and visual cryptography. This paper proposes a technique of processing the signature of a customer and then dividing it into shares. Total number of shares to be created is depending on the scheme chosen by the bank. When two shares are created, one is stored in the bank database and the other is kept by the customer. The customer has to present the share during all of his transactions. This share is stacked with the first share to get the original signature. The correlation method is used to take the decision on acceptance or rejection of the output and authenticate the customer.

An address assignment for the automatic configuration of mobile ad hoc networks

Mobile ad hoc networks (MANETs) are infrastructure-less, multi-hop wireless networks, which can be deployed without any pre-existing setup. MANETs are mobile in nature and any node can join and leave the network at any time. Due to mobility, MANETs must be able to configure themselves without human intervention. Configuration (such as address assignment) of a node in such a network is an important issue. In this paper, we present a solution for address assignment, which is distributed in nature and can be used for IP address configuration in MANETs. Each node can allocate the address independent of others. Although our solution uses broadcast messages, results show that by fixing a few parameter values we can reduce the number of broadcast messages. We simulate the protocol and results show that our solution yields better performance those of the earlier algorithms.

Line coverage of path delay faults

We propose a new coverage metric for delay fault tests. The coverage is measured for each line with a rising and a falling transition, but the test criterion differs from that of the slow-to-rise and slow-to-fall transition faults. A line is tested by a line delay test, which is a robust path delay test for the longest sensitizable path producing a given transition on the target line. Thus, the test criterion resembles path delay test and not the gate or transition delay test. Yet, the maximum number of tests (or faults) is limited to twice the number of lines. In a two-pass test-generation procedure, we first attempt delay tests for a minimal set of longest paths for all lines. Fault simulation is used to determine the coverage metric. For uncovered lines, in the second pass, several paths of decreasing lengths are targeted. We give results for several benchmark circuits.

Dynamic Association Rule Mining using Genetic Algorithms

A large volume of transaction data is generated everyday in a number of applications. These dynamic data sets have immense potential for reflecting changes in customer behaviour patterns. One of the strategies of data mining is association rule discovery which correlates the occurrence of certain attributes in the database leading to the identification of large data itemsets. This paper seeks to generate large itemsets in a dynamic transaction database using the principles of Genetic Algorithms. Intra Transactions, Inter Transactions and Distributed Transactions are considered for mining Association Rules. Further, we analyze the time complexities of single scan technique DMARG (Dynamic Mining of Association Rules using Genetic Algorithms), with Fast UPdate (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that the algorithm DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated.

Evolutionary approach for mining association rules on dynamic databases

A large volume of transaction data is generated everyday in a number of applications. These dynamic data sets have immense potential for reflecting changes in customer behaviour patterns. One of the strategies of data mining is association rule discovery, which correlates the occurrence of certain attributes in the database leading to the identification of large data itemsets. This paper seeks to generate large itemsets in a dynamic transaction database using the principles of Genetic Algorithms. Intra transactions, Inter transactions and distributed transactions are considered for mining association rules. Further, we analyze the time complexities of single scan DMARG(Dynamic Mining of Association Rules using Genetic Algorithms), with Fast UPdate (FUP) algorithm for intra transactions and E-Apriori for inter transactions. Our study shows that DMARG outperforms both FUP and E-Apriori in terms of execution time and scalability, without compromising the quality or completeness of rules generated. The problem of mining association rules in the distributed environment is explored in DDMARG(Distributed and Dynamic Mining of Association Rules using Genetic Algorithms).

A Simulation-Based Test Generation Scheme Using Genetic Algorithms

This paper discusses a Genetic Algorithm-based method of generating test vectors for detecting faults in combinational circuits. The GA-based approach combines the merits of two techniques that have been used previously for generating test vectors - the directed search approach and the random test method. We employ a variant of the traditional GA, the Adaptive GA (AGA), to improve the effi cacy of the genetic search. Two cost functions that are used for assessing the quality of the vectors are discussed. The performance of the AGA-based test generation approach has been evaluated using ISCAS-85 benchmark circuits. In our approach, the number of vectors that need to be simulated for detecting all detectable faults is significantly smaller than that required for a random test method. Even when optimized input distributions are used to generate the random test vectors, the AGA sustains its superior performance over the random test method.