International Journal of Academic Research in Computer Engineering
Volume:1 Issue: 2, Nov 2016

With the rapidly increasing popularity of XML, an effective method to transmit XML data over wireless broadcasting environments is urgently required. Secure broadcasting of XML data is becoming an essential requirement for many applications in mobile wireless networks. Several indexing methods have been proposed to reduce the tuning time in processing the XML queries over the wireless XML stream. Tuning time is the sum of period of times which a mobile client stays in active mode in order to retrieve the required data over the wireless stream. Therefore, it is frequently used to estimate the energy consumption of a mobile client. The problem of existing indexing methods is that they cannot directly be applied to an encrypted XML stream since mobile clients can only access the authorized parts of the XML data in an encrypted XML stream. In this paper, we define a new structure for XML stream which supports data confidentiality of XML data over the wireless broadcast channel. We also define an access mechanism for our proposed structure to efficiently process XML queries over the encrypted XML stream. The experimental results demonstrate that the use of our proposed structure and access mechanism for XML data broadcast efficiently disseminates XML data in mobile wireless networks.

As the CMOS technology is being scaled down further into the nanoscale, we are witnessing lot of short channel effects which are resulting in considerable deviation from its normal behavior. Also, the leakage current is increasing which is leading to power dissipation issues is small chips. Because of this many alternative technologies are being explored with an intent to replace the existing CMOS technology. Carbon Nanotube Field Effect Transistor (CNTFET) and Graphene Nanoribbon Field Effect Transistor (GNRFET) are among the technologies which are being widely studied and has been proposed to be a likely candidate to replace the MOSFET technology. In this paper, we have done extensive simulations on CNTFET and GNRFET, looking at how some of the device parameters affects the performance of the CNTFET and GNRFET. We have used the HSPICE and CosmosScope tools for the simulation and verifying the graphs. The CNTFET simulations were done using the model developed by the Stanford University and the GNRFET simulations using the HSPICE model developed by the University of Illinois. The next part of our research was concentrated on comparing the CNTFET and GNRFET with the MOSFET technology. The MOSFET technology was simulated by using the PTM models developed by the Arizona State University. Basic gate like inverter, NAND, NOR and XOR were built using all the three technologies for channel lengths 7nm, 10nm, 16nm and 32nm. The HSPICE and Cosmoscope were then used to find average dynamic power, delay, leakage powers, EDP and PDP in each case to compare between the different technologies. The results showed us that the CNTFET and GNRFET offers reduced power consumption when compared to the MOSFET and also operates at higher speed.

Software testing is considered one of the most important stages in the software development life cycle aiming to detect all software faults including different methods and approaches such as regression test method. When new alterations or features are added to software, the regression test examine the system to ensure that no software operation has been affected by new modification. Test case prioritization is an approach for organizing test cases with a means to minimize time consumption, cost, and efforts, and to provide faster fault detection. Multiple criteria-based prioritization techniques have the potential of the better improvement and enhancement of the effects of regression test than other techniques applying single criteria. In this paper, an overview of regression test and prioritization will be presented along with an introduction to multi-objective techniques used in prioritizing. Furthermore, a comparison will be made between single- and multiple-criteria-based prioritization methods.

This paper presents an application of Cuckoo Optimization Algorithm (COA) in game theory and multi-objective optimization problems. The recently introduced COA has proven its excellent capabilities, such as faster convergence and better global optimum achievement over conventional optimization algorithms. In this paper COA is used to find Nash Equilibrium points of nonlinear non-cooperative games. The introduced method can also be used as an alternative approach to solve multi-objective optimization problems. The effectiveness of the proposed method, in comparison to genetic algorithm and Colonial Competitive Algorithm (CCA), is proven through several static and dynamic example games and also multi-objective problems.

With the development of computer networks and in particular cloud computing and fast-moving organizations into unified information, the need for a secure environment for managing data in the Data Storage (DS) is felt. What distinguishes the cloud space for organizations in terms of security from traditional models is the lack of direct control of information and the probability of their improper management by cloud servers that can be considered the most important threat in the cloud computing model. DS plays a strategic role in the development of applications and communications in a cloud environment. As you know, the data can be in the form of text, audio, video, and etc. DS is used by combining a set of hardware and software instruments to store information and databases on the Web. The type of hardware used in DS and type of backup, the operating system and certain security and commercial applications set on it, will endow security and functionality to customers. The most important discussion in DS and its relationship with cloud environment is security; in this article, we will study data security in DS.

Cloud computing is a scalable and distributed computing environment which a large collection of virtual computing resources, variety of infrastructure and software are offered to customers via the Internet as a service. Nowadays cloud computing due to reducing costs, high flexibility and expandability is one of the most attractive technologies in the world, but in the meantime user's privacy and data security is very essential and critical in cloud environment. The purpose of this paper is to check users’ authentication in the service layer as one of the most effective solutions for increasing the security in cloud computing. In general, the purpose of authentication is to ask permission of users and lack of unauthorized access to the cloud environment. In fact, this is a process of identification, authentication, allowing users to enter the system, which consists of two identification and authentication phases. Identification is responsible for the users’ authentication. This identification is often defined as a username for the system, and it recognizes users through it. There are several ways to authenticate in the cloud environment. Each authentication model has its own methods and unreliability. In this paper, for the data security and identifying the users authorized to enter the cloud environment we will study cloud multi-level authentication and data cryptography.

Medical images segmentation due to the increasing volume of this images is difficult and in accessible to human. with development of image processing we can use computers to help. Segmentation of the early stages of image processing are very well regarded. In this paper, a combination method based on level set and active contour models have been proposed to achieve more accurate image in image processing. The selected dataset contains different slices that in this article 8 slices have been selected for testing. The proposed algorithm was applied on these slices and it has been compared with previous methods. The results have been tested on MRI images of the brain and this results show that the proposed method is better than other methods proposed before.

Recovery images as one of the major issues in the field of image processing has many applications, which in recent decades has been welcomed by many researchers. In this paper, also features content are used to retrieve the images that these features are extracted by Hierarchical Geometric Centroids method, so after extracting them, these features in the proposed method are used to retrieve the images. The proposed method is composed of two algorithms of Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Discriminant Analysis (DA) that to evaluate it two criteria of accuracy evaluation and recalling were used that the results of the proposed method with two MLP ANN and DA algorithms have been studied. Results showed that the proposed method has the Precision rate of 0.931 and recall rate of 0.943, and MLP ANN algorithm has the Precision rate of 0.751 and recall rate of 0.758 and DA has the Precision rate of 0.681, and recall rate of 0.651.

String matching plays a major role in our day to day life be it in word processing, signal processing, data communication or bioinformatics. Approximate string matching is a variation of exact string matching that demands more complex algorithms. As the name suggests, in approximate matching, strings are matched on the basis of their non-exact similarities. Quantification of the parameter “similarity” and its efficient computation received a lot of attention in the recent past. Many definitions of similarity have been given depending on application needs. One of the standard ways of quantifying similarity is using the well-known edit distance. It is defined as the number of edits (insertions, deletions, and substitutions) needed to go from one string to another. If similarity or distance has a fuzzy co-efficient where “Hat” and “Fat” are considered the closest words due to the adjacency of the alphabet keys H and F to G. In a similar way, fuzzy concepts can be applied for a DNA sequence similarity where evolution is rule based. Many algorithms based on binary tree or improvement on it as suffix tree, dynamic programming, indexing of strings before searching are being used. Neural network gives a very good result as it is efficient to learn and eliminate case based errors. Further a fusion of fuzzy sets at the input, output and the neurons can greatly improve the matching process.