Information Technology

Multiview video coding is an extension of H264/AVC. When an Multiview video coding bitstream is decoded, some views (named target views) are to be is played; some other views (named dependent views) may not be displayed but are needed for inter-view prediction of the target views. The original Multiview video coding design requires pictures of the dependent views to be fully decoded and stored. This entails both high decoding complexity and high memory consumption for the pictures in the views which are not intended for display, particularly when the number of dependent views is large. In this paper, a single motion compensation loop decoding scheme is introduced to address these disadvantages. The proposed scheme requires only partial decoding of pictures in dependent views and thus significantly reduces decoding complexity and memory consumption. The proposed method is based on the motion skip, wherein inter-view motion and coding mode prediction is exploited. Simulation results shows that the proposed scheme provides a substantial reduction of complexity and memory size, at the expense of only a minor compression efficiency loss, compared with multiple motion compensation loop decoding schemes for Multiview video coding.

Population based stochastic search techniques inspired by nature has attracted much attention recently as an effective approach for solving numerical optimization problems. Two of the most commonly used population-based global optimization algorithms are PSO and DE. Although both have been successfully applied to a wide range of test and real life problems but they have certain shortcomings associated with them. Since DE utilizes the differential information to get the new candidate solution, sometimes it results in instability of performance. Again DE has no mechanism to memory the previous process and use the global information about the search space, so it results in a waste of computing power and may get trapped in local optima. Although PSO converges quickly but, easily gets stuck in local optima because of loss of diversity of swarm. In this paper a simple hybrid version of DEPSO has proposed. DEPSO combines the differential information obtained by DE with six mutation policies and the memory information extracted by PSO to create the promising solutions. Again a time decreasing parameter tuning has been proposed which brings in a significant improvement of the performance of DEPSO. Performances are presented through the tests of four complex benchmark functions with three different dimensionalities. The experimental results show that the convergence rates and qualities of solutions are greatly improved.

Software Reliability Growth Model (SRGM) is a mathematical model of how the software reliability improves as faults are detected and repaired. The performance of SRGM is judged by its ability to fit the software failure data. How good does a mathematical model fit to the data and reliability of software is presented in the current paper. The model under consideration is the, G-O model. A two step approach is used to estimate the model parameters by combination of Maximum Likelihood Estimation (MLE) and Least Square Estimation (LSE) methods. To assess the performance of the considered SRGM, we have carried out the parameter estimation on the real software failure data sets.

Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. A major feature of the cloud services is that user’s data are usually processed remotely in unknown machines that user’s do not own or operate. While enjoying the convenience brought by this new emerging technology, user’s fears of losing control of their own data (particularly, financial and health data) can become a significant barrier to the wide adoption of cloud services. To address this problem, a highly decentralized information accountability framework is proposed to keep track of the actual usage of the user’s data in the cloud. In particular, an object-centered approach that enables enclosing the logging mechanism together with user’s data and policies. The JAR programmable capabilities are leveraged to both create a dynamic and traveling object, and to ensure that any access to user’s data will trigger authentication and automated logging that is local to the JARs. To strengthen user’s control, a distributed auditing mechanism is proposed to be implemented.

Since, Islam is the Fastest Growing Religion, but, why do People judge Islam so negatively in the Internet. Major Objective was to understand the use of the Internet to propagate Islam and Supportive Objectives were to find the new trends of propagating Islam and to find challenges facing by Islam. This study is based on qualitative and quantitative methodology which was carried out by employing primary and secondary data. The Primary data has been collected through questionnaire, interviews. Ten people have been interviewed as key informants. The Secondary data was collected from relevant books, websites, reports, journals and news paper articles and other relevant documents. Internet is an emerging trend to propagate Islam over the world. Ninety percent of all users go to the Internet for news or information; of that 90 percent, 80 percent use the Internet for research. One in four of these users surfs the Internet for religious and spiritual material, with 23 percent specifically searching for information about Islam.

Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. The scheduling problem is computationally hard even when there are no dependencies among jobs. Thus, the new local search (LS) and particle Swarm Optimization (PSO) algorithm seems to be efficient for the problem of batch job scheduling on computational grids. We consider the grid scheduling as a bi-objective optimization problem consisting of the minimization of the makespan and flowtime. The bi-objectivity is tackled through a hierarchic approach in which makespan is considered a primary objective and flowtime a secondary one. In this, a heuristic approach based on particle swarm optimization algorithm is adopted for solving task scheduling problem in grid environment. Particle Swarm Optimization (PSO) is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The computational results show that our PSO & TS implementation clearly outperforms the compared algorithms. This work proposes optimization technique called Tabu search that is combined with the ant colony optimization and PSO technique to solve the grid scheduling problems.

XML makes data flexible in representation and easily portable on the Web but it also substantially inflates data size as a consequence of using tags to describe data. Although many effective XML compressors, such as XMill, have been recently proposed to solve this data inflation problem, they do not address the problem of running queries on compressed XML data. More recently, some compressors have been proposed to query compressed XML data. However, the compression ratio of these compressors is usually worse than that of XMill and that of the generic compressor gzip, while their query performance and the expressive power of the query language they support are inadequate. In this paper we propose our approach a XML compressor which support querring compress XML data with partial de-compressor. Our approach address the compressor time and adaptive compression ratio of existing XML compressor.

In this paper we have tried to implement the idea to classify fingerprints through identifying the core of the fingerprint. The fingerprint is taken as an input via an image. The image is then transformed for necessary preprocessing. Then we make a graph with ridges’ ending and bifurcations all around the Centre, acting as vertices. Then we compare the graph constructed using the connection of vertices. The idea is to calculate distance between neighbouring vertices for each vertex. Our aim is to increase the efficiency of fingerprint identification in different orientation.

The aim of this article is to analyze the role of metadata in the use of learning objects. Firstly, an overview of metadata is provided and the role of educational metadata for learning objects is considered. Then the purposes, the uses and the values of metadata are investigated in this regard. In the continuation, the study reviews other related issues such as metadata categories, metadata creation, and metadata attributes or characteristics. Finally a collaborative approach for metadata creation is proposed. This research concludes that metadata has an invaluable role in facilitating the widespread use of learning objects.

Expert systems, which are the most commercially successful result of research in artificial intelligence, are software entities that follow the cognitive abilities of human experts in complex decision making situation. As one of the primary activities in computer science and dependent heavily on the rapid development in computer technology, Expert systems have been eagerly adopted by industries and applied to a wide range of applications. Expert systems belongs to a field of intelligence knowledge based systems the constitute one of the principle field of activity of computational intelligence, a field which been referred to as the science that attempts to reproduced human intelligence using computational means.