Current issue of "International Review on Computers and Software (IRECOS)"

by B. Muthusenthil, S. Murugavalli

Abstract - In mobile ad hoc network (MANET), the increased node mobility causes increased control overhead. The existing route discovery technique is not efficient for intermittently connected or Delay Tolerant Network (DTN). In order to overcome these issues, in this paper, we propose location aided cluster based geographical routing protocol for intermittently connected MANET. In this technique, cluster head is chosen based on node value which is estimated based on degree difference, node mobility and residual energy. The cluster consists of GPS enabled node and antenna equipped node. The cluster that contains atleast one G-Node considers the remaining energy, speed of the node along with the mobility of node to select the cluster head. Also the cluster maintenance is implemented in order to re-organize and re-configure the cluster dynamically due to the mobility of nodes in the ad hoc networks. Then a store-carry forward and geographical routing based routing protocol is employed. Finally, in order to prevent the location error caused during routing, a location update technique is executed. By simulation results, we show that proposed technique minimizes the control overhead and delay.

Keywords: Geographical Routing, Clustering, Location Based Information, Mobile Adhoc Networks.

by Sandhya M. K., Murugan K.

Abstract - False data elimination during data aggregation and transmission is important in ensuring data integrity in wireless sensor networks. The existing security mechanisms do not address the uneven battery drain due to funneling effect. In this paper, False Data Elimination - Mobile Data Collector scheme is proposed to eliminate the injected false data by multiple compromised nodes during data aggregation and transmission and to resolve the uneven battery drain due to funneling effect by using a mobile data collector. It ensures confidential data transmission and provides immunity against many attacks. Simulations indicate that it has a higher false data filtering capacity, lesser energy consumption and improved network lifetime.

Keywords: Data Aggregation, False Data Elimination, Funneling Effect, Mobile Data Collector, Network Lifetime, Wireless Sensor Networks.

by Sanjay Kumar Suman, Dhananjay Kumar, L. Bhagyalakshmi

Abstract - One of the recognizable features in wireless ad hoc networks is lack of any central coordinator in which every node selects its own transmission power. As a result, some nodes transmit at the maximum power and impose extra interference on other nodes. To combat the interference, other nodes also behave the same manner which causes faster discharge of battery power and results poor connectivity. Many algorithms have been proposed in game theoretic framework to alleviate this problem and most of which are based on linear pricing scheme. This paper proposes a new pricing scheme in non cooperative power control game for wireless ad hoc network which not only restricts the nodes using maximum transmission power but also ensures adequate quality of services. The existence of Nash equilibrium is proved mathematically. It has been shown that, beyond this point nodes don�t get any benefit by increasing their transmission power. Through the numerical results it is demonstrated that with this proposed scheme (NCPCG) the nodes are encouraged to consume less transmission power for their mutual benefits while still achieving the maximum utility.

Keywords: Interference, Nash Equilibrium, Non Cooperative Power Control Game, Pricing, Transmission Power Control, Wireless Ad Hoc Networks.

by Marzanah A. Jabar, Mustafa S. Khalefa, Rusli Hj. Abdullah, Salfarina Abdullah

Abstract - Ontology is an important concept in Computer Science to formally represent knowledge. The software engineering ontology assists in defining information for the exchange of semantic project information framework. Research into ontological issues has been widely active in various areas. This paper presents the origin of ontology research and gives the different definitions of ontology. The paper gives an overview of ontology and its types including the building and design for an enterprise system.This review tries to study articles in which adaption model and there properties were discussed in order to get a clear review about the ontology. This study is showed a Systematic Literature Review which was used to identify important characteristic about the ontology. The research identified more than 70 paper on this study but only 46 of them was precisely relevant in the field of Ontology development processs. In this paper we analyse the ontology based on types, design, building ,model as a systematic review of the subject. And choose the best way according to that.

Keywords: Ontology, Types of Ontology, Building, Design of Ontology.

by R. Senthil Kumar, C. Sureshkumar

Abstract - Mobile ad hoc networks (MANET) also as called auto configure network, in which mobile devices are connected as a wireless node. Due to changes in topology and mobility in MANET, it is suitable to use in environment that need of on fly set-up. The design of energy efficient cluster based on-demand routing protocol and to increasing the life time of node it is a very difficult task in mobile ad hoc networks. Ad hoc On-demand Distance Vector Routing Protocol (AODV) for MANET were employed on the route discovery process to establish routes between two mobile nodes. One of the most important issue is the link break occurred by the nodes due to mobility in ad hoc routing protocol. In a network when nodes establishing the connection between the nodes, they utilize more energy in route discovery process, then the node will be out of energy. In this paper, we proposed a new idea to Minimizing the Route Rediscovery Process based on cluster Techniques (CBMRRP) by scheming the source node RREQ packet to select the more stable route i.e. cluster head selection. The proposed protocol has two schemes, first one is the selection of cluster heads based on Received Signal Strength(RSS) to minimizing the route rediscover process and the route optimization of flooding process , based on Time-to-Live (TTL) value ,and the second one is by reducing route rediscovery process solution to accomplish the link failure. The analytical experiment was done by both probability and correlation techniques and simulation was done on ns-2.34 platform and compared with the original AODV. By verifying the result, the effectiveness and network performance of Quality of Service (QoS) are improved.

Keywords: MANET, Cluster, RREQ, RSS, TTL.

by M. Devi, S. Chenthur Pandian

Abstract - A primary concern in mobile impromptu networks (MANETs) is security. Not like typical infrastructure based mostly wireless networks like, wireless cellular net-works, Mobile ad hoc Networks (MANET) contains speedily deployable, self organizing and self maintaining capability options. Moreover, they will be shaped on the ﬂy as required. Additional issues for MANET�s security arise as a result of its high quality to its users. A number of key management schemes have been proposed for MANETs. Due to the disadvantages in Shamir secret sharing such as distribute erroneous sub shares based on this that message transmission communication cost is increased. To overcome this Verifiable secret sharing schemes are used here. Existing research in key management can only handle very limited number of nodes and are inefficient, insecure, or unreliable when the nodes increases. Here this paper modifies Autonomous key management scheme is proposed to address both for security and efficiency for key management in Mobile Ad hoc Network (MANET). It also reduces communication and computational cost in Ad-Hoc Network and works for large number of nodes.

Keywords: Mobile Ad Hoc Network, Verifiable Secret Sharing Schemes, Update Operation, Join Operation, Leave Operation, Merge Operation, Partition Operation, Expansion Operation.

by K. Sathesh Kumar, M. Hemalatha

Abstract - The tremendous growth in web-based technology, application and sharing of information, and knowledge discovery which have a direct impact on economy, presents voluminous data which require Data Mining Techniques. The current study presents a novel framework of data mining which clusters the data and then follows the Fuzzy Association Rule Mining. The first stage employs the Fuzzy Ant System-Based Clustering Algorithm (FASCA) and Fuzzy Ant K -means (FAK) to cluster the database, while the Fuzzy ant colony system-based Fuzzy Association Rules Mining algorithm can be applied to discover the useful rules for each group. The evaluation revealed that the intended method was not only able to mine the rules much more rapidly, but can also identify more significant rules.

Keywords: Clustering, Data Mining, Fuzzy Ant Colony System, Fuzzy Association Rule.

by V. Gomathi, V. Ramachandran

Abstract - The electrical grid is at the threshold of a revolution and the outcome of research on Internet Technology will play a major role in transforming the nature of the electrical grid. To come up with a new paradigm in the field of electrical grid, a distributed architecture that uses the concepts popularized in Internet research is required. Electrical grid consists of several power system services that are to be coordinated to obtain a specific response. Yet the major problem is that different services from different power sectors are to be coordinated where the services have been developed and implemented in different platforms and deployment environments. In this paper a generalized deployment independent service oriented architectural model has been proposed for solving various power system problems. This architectural model allows flexible way to plug-in new power system services and the existing services can be customized only by modifying respective XML schemas. The power system clients geographically apart can access these services whenever required.

Keywords: Optimal PMU Placement, Power System, Service Oriented Architecture, SOAP, XML Schemas, Componentized Model.

by R. Moussaoui, H. Medroumi, F. Moutaouakkil

Abstract - After studying the existing wheeled kinematic solutions of mobile robotics, it turned out how we can provide maximum independence of movement with a mobile robot while focusing on its architecture. In this paper we have modelized our architecture control using Auml rating and based on Multi agent System Engineering Methodology. Our architecture take into consideration issues related to the security of communication and combine the capacity of an intelligent agent in order to make to the robot maximum of autonomy and learning .That it is used in general in the field of environmental monitoring, more specifically the use of robots to perform commands related to the operating environment, data collection and reporting.

Keywords: Multi-Agent Systems, Sensors, Actuators, MaSE (Multi-agent Systems Engineering Methodology), Control architecture, Internet, Agent UML.

by P. Asha, T. Jebarajan

Abstract - Data mining techniques that extract information from huge amount of data have become popular in many applications. Many algorithms are designed to analyze those volumes of data automatically in efficient ways. To improve the performance a data mining task, it is important that parallelism would be better than the sequential mining. Association Rule Mining (ARM) is data mining technique which aims to discover patterns/rules among items in a large database of variable length transactions. This paper proposes a parallel Frequent Pattern Tree Growth algorithm. Task parallelization is done by partitioning the database and sent to all of its compute nodes and finally the results were merged together in the Head node. Efficient partitioning and parallelization works in a better way and shows good performance. Filtering of the retrieved association rules using various Rule Interestingness measures has been done. The Performance of the parallel FP Tree algorithm is then compared and analyzed with the Rapid Miner Toolkit.

Keywords: Frequent Patterns Mining, Sequential Mining, Parallel Processing, Association Rule, Interestingness Measures .

by J. C. Smitha, S. Suresh Babu

Abstract - The most necessary part of the living things which standardizes and manages other organs is the brain. The brain may get affected through any disease if the patient is not in a normal condition. Therefore it is significant to examine the condition of the brain. In the region of brain MRI image deformity fragmentation, various research works were made. However these research efforts presentations are needed in the image pre-analysis. During pre-analysis brain via MRI brain images for identifying the deformity, it is essential to examine the acquired patient�s image in detail. An error treatment will be specified to the influenced patient if the study may have any error. So there is a necessity to develop precision in the deformity segmentation by achieving the fundamental pre-analysis in the MRI images. A combined approach with MRI brain image abnormality segmentation and denoising process is proposed in this paper. The proposed technique comprised of five stages namely, (i) Preprocessing, (ii) Feature Extraction, (iii) Image Classification, (iv) Segmentation and (v) Tissues Classification. Initially the database images are given to the preprocessing stage, for removing the noise. In preprocessing, the denoising process is performed it increases the segmentation and feature extraction accuracy. After the preprocessing, the image features are extracted to classify the images in the image database into normal and abnormal. After the image classification, the abnormal MRI images abnormal tissues like stroke, trauma and tumor are segmented. For this, the features are extracted from the segmented abnormal tissues. In the proposed technique, three features such as modified entropy, energy and innovative feature are extracted in the feature extraction stage. By using these extracted features, the abnormal tissues are classified by using a well known classification technique called Feed Forward Back Propagation Neural Network (FFBNN). The implementation results show the effectiveness of proposed MRI abnormality tissues segmentation technique in segmenting and classifying the MRI images and the achieved improvement in the segmentation and classification result. Furthermore, the performance of the proposed technique is evaluated by comparing with the existing MRI image segmentation techniques.

Keywords: MRI Images, Skull Stripping, Segmentation, Feed Forward Back Propagation Neural Network (FFBNN), Average Filtering, Energy, Entropy.

by T. Rachad, S. Elghazi, J. Boutahar

Abstract - Nowadays, e-commerce knows a more extensive use by the internet users. Facing the growing number of companies adopting e-commerce and the large number of customers who often lack experience in research products and services, search engines lose their effectiveness and performance. In this context, centralized systems called mediation systems have been proposed to integrate data sources from a few suppliers and help users quickly find their needs. These systems are complex, closed, too centralized and do not assist users in their research. The combined use of web services technologies and intelligent agents can help to overcome these problems. In this paper we propose a Service and Agent based mediation system architecture (SAMED) that use the agents and web services technologies to facilitate mediation at the web and especially in e-Commerce domain. It uses a standard abstract service model that shows how to interact with the provider�s services concretes implementations. Then, the invocation of a service can only be achieved through an agent that will encapsulate a service to make it dynamic and autonomous. This type of agent will be considered as a standalone service.

Keywords: Mediation Systems, Multi-Agent Systems, Web Services, E-Commerce.

by K. Suresh Kumar, T. Sasikala

Abstract - The major motto of my research is to develop a technique for web security using mutual authentication and clicking and cropping based image CAPTCHA technology. In our technique, we use two sections as registration and login. To create an account to use the application we use the registration section and to access the application we use the login section. We set five mandatory fields to login the application. The mandatory fields we give in login section should similar to the mandatory fields we gave while registration. The mandatory fields are checked with respect to the user id. The mandatory fields we set are user id, password, selecting image, number of clicks on image and cropping image. If the fields are same in the login section and registration section for a particular user id, the system will allow the user to access the application. Here, we incorporate three different features than the usual login section in the applications. The different features are selecting an image from a set of images and doing number clicks on that selected image and cropping a portion in that image. Our technique enhances the web security because of these added features.

Keywords: Web Security, CAPTCHA, Mutual Authentication, Cropping-Clicking.

by K. Rama Abirami, M. G. Sumithra, J. Rajasekaran

Abstract - In Mobile Ad Hoc NETwork (MANET), an attacker can easily make a server or a network resource unavailable to users by temporarily interrupting or suspending the services of a host connected to the Internet. So far, several secured routing protocols have been proposed for MANET. But all of them have certain disadvantages. Hence, security in MANET is still a challenging area. An Efficient Secure Enhanced Routing Protocol (ESERP) for MANET is proposed which is an attack resistant authentication mechanism. The problem definition is that the proposed protocol focuses on efficient security against protocol exploitation flooding attacks which is a part of Distributed Denial-of-Service (DDoS) attacks in MANET routing protocols. The ESERP protocol is used to address all the issues of protocol attacks in DDoS attacks in MANET for route establishment. The Protocol exploitation flooding attacks in DDoS includes SYN floods, fragmented packet attacks, Ping of Death, Smurf DDoS and more. Detailed simulation studies have confirmed the efficiency and effectiveness of ESERP.

Keywords: DDoS, ESERP, MANET, Routing Protocols, Security.

by R. Lavanya, V. Ramachandran

Abstract - An innovative SOAP communication model with modified message exchange pattern is proposed for enhancing the security of Video on Demand system. This model insists the video requester or the video provider to give the request initially to an authentication service provider for a video file to be retrieved or to be uploaded. The response of the authentication service provider is an encrypted messageID based on the credentials of the client, which is enclosed in a SOAP envelope and this response is forwarded as SOAP request to the video service provider in order to access the appropriate service. Since the proposed model extends the original behaviour of message exchange pattern, an exclusive AXIS based Video on Demand system is implemented which ensures security.

Keywords: Video on Demand, SOAP Communication, Message Exchange Pattern.

by M. Ambika, K. Latha

Abstract - With the rapid growth of the World Wide Web, the availability of web- based information is also increasing exponentially day by day. We can say that �we are drowning in data, but starving for knowledge�. Thus, considering the impressive variety of the web, retrieving interesting, relevant, and required information has become a very difficult task. A popular and successful technique that has shown much promise is web mining. This paper presents a deep and intense study of various techniques available for web mining. We also focus on the comparative study of various techniques, process, and algorithms. Finally, we conclude with applications and some research issues. This report also discusses how these approaches facilitate the use of the Internet.

Keywords: Web Mining, Web Content Mining, Web Structure Mining, Web Usage Mining .

by Marwa Chouchene, Fatma Sayadi, Mohamed Atri

Abstract - In this paper we present an integral image algorithm that can run in real-time on a Graphics Processing Unit (GPU). Our system exploits the parallelisms in computation via the NVIDA CUDA programming model, which is a software platform for solving non-graphics problems in a massively parallel high performance fashion. We compare the performance of the parallel approach running on the GPU with the sequential CPU implementation across a range of image sizes.

Keywords: Integral Image, GPU, CPU, NVIDIA CUDA.

by K. Haridas, Antony Selvadoss Thanamani

Abstract - The construction of large database with thousands of data has been facilitated by the developments in data storage and image acquisition technologies. Suitable information system requires proper handling of these datasets in efficient manner. Content-Based Image Retrieval (CBIR) is commonly used system to handle these datasets. Basis on the image substance CBIR extracts the images that are relevant to the user given query image from large image databases. Many of the CBIR systems retrieval of the result are corresponding to feature similarities for user given query, ignoring the similarities among images in database. These existing CBIR system measures the feature similarities by using k means algorithm, but the traditional k-means algorithm mostly depends on the selection of initial centers values, the algorithm normally uses random procedures to get them and it degrades the performance of the CBIR retrieval results. To overcome the problem of initial centroid random selection process in K means clustering algorithm use the fuzzy logic based feature similarities information with K means clustering algorithm to image retrieval system. Combining both low-level and high-level visual features, the fuzzy k means algorithm entirely measures the features similarities information between the images in larger dataset. Fuzzy k means clustering algorithm optimizes the relevance results from conventional image retrieval system by firstly clustering the related images in the images database to improve the effectiveness of images retrieval system.

Keywords: Image Clustering, Fuzzy K Means, K Means Unsupervised Classification, Content Based Image Retrieval (CBIR).

by R. V. Rajesh, S. Arif Abdul Rahuman, J. Veerappan

Abstract - The panorama of this paper is level set approach which has got skillful mechanism of grasping the image region from the input image. So now the exemploriest software mechanism of this paper is input image extracted by level set segmentation and we will compute the impeccable histogram value of the segmentation image. And also we have got a protocol that stored database either online or offline (Two dimensional or Multidimensional) the every reference image has got impeccable histogram value. The second stage of this paper is the histogram values of input image and referenced image are compared. If both are impeccably matching we will undergo by trainer which has got iterations of computation mechanism. So we can find the result by matching input image and referenced image. This is the stepping stone for all pattern matching analysis and also data which is Two or Multidimensional. And also for parallel processing in super computer fast mechanism.

Keywords: Level Set Approach, Region Based Image Retrieval, Color-Size Histogram (CSH), Earth Mover Distance (EMD).

by B. Mansouri, Z. Chama, A. M-Djafari

Abstract - Fourier Synthesis inverse problem consists in reconstructing an image from the measured data which correspond to partial and noisy information of its Fourier Transform. This inverse problem is known to be nonlinear and ill-posed. It then needs to be regularized by introducing prior information. In this paper we propose two priors information. In the first prior information, we assume that the original image is composed by homogeneous regions, so in this case we propose the Hidden Markov Modeling dedicated to classification which is the most appropriate distribution for the image labels in a Bayesian framework. In the second prior information we assume that the noise is a Gaussian centered and in order to improve the quality of image reconstruction we introduce a total variation algorithm with a Bayesian analysis to regularize the solution. Appropriate Markov Chain Monte Carlo algorithms are proposed to implement our approach. This method is applied on synthetics and real images.

Keywords: Fourier Synthesis, Image Reconstruction, Hidden Markov Modeling, Totalvariation, Bayesian Analysis, MCMC Algorithms.

by P. Kannan, R. Shantha Selva Kumari

Abstract - Face detection is the first step in the face recognition system. The Neural network as well as Gabor wavelet based face detection is proposed in this paper. The proposed system applies neural network algorithm to make the slanting face upright and Gabor wavelet is used to extract the invariable features from the selected window. The principal component analysis is applied to identify the person from the data base. The Proposed system gives very high detection efficiency with low number of false alarms. We have shown the results on different test sets with different degrees of variability of the face patterns. In this paper, we present a new approach for determining the orientation of face in the selected window and then use this information to rotate the face upright.

Keywords: Face Detection, Artificial Neural Networks, Gabor Wavelets, Principal Component Analysis, Machine Vision.

by S. Karthikeyan, N. Rengarajan

Abstract - Retinal Nerve Fiber Layer (RNFL) evaluation is important for diagnosis of glaucoma in the earlier stage as RNFL changes precedes visual field loss and optic disc changes. An early detection of changes in the texture caused by nerve fibers is important in the diagnosis of glaucoma. Red free fundus image are preprocessed, and region of interest is cropped in the inferior and in the superior region of the fundus image around the optic disc and regional profile analysis is performed to identify the RNFL defect region. Moment based intensity features, Wavelet based second order textural features using Gray Level Co-occurrence Matrix (GLCM) and wavelet based energy features are extracted from the segmented images. Extracted features are fed as input to Adaptive Neuro Fuzzy Inference System to classify the images as normal or glaucomatous eye. The method achieves 98.3% sensitivity, 96% specificity and 97.23% accuracy and can be used as a decision support system for clinical diagnosis.

Keywords: Retinal Nerve Fiber Layer, Glaucoma, Wavelets, Texture, ANFIS.

by S. Meenakshi, V. Venkatachalam

Abstract - The classifications of uncertain data become one of the tedious processes in the data mining domain. The uncertain data are contains tuples with different probability distribution and thus to find similar class of tuples is a complex process. When we consider uncertain data, the feature vector will not be a single valued but a function. Recently, different methods are proposed on decision tree based uncertain data classification with binary based operation on the decision tree. When multiclass data are given to the decision tree, their algorithm has to give repeated calculation to produce the probability distribution matching the class labels, thus time and memory utilization will be high for the particular algorithm. In this paper, we have intended to propose a classification method for uncertain data based on the decision tree. The proposed approach concentrates on an adaptive averaging method, where we have incorporated mean and median of the tuple to produce the feature value that will be used in the decision tree for decision making. Then a probability calculation is executed to find the relevance of tuple with respect to a class. If the calculated probability value is similar to a particular probability distribution, then the tuple is marked to that particular class. Thus, we produce a decision tree with c number of leaf nodes, where c is the number of class labels in the training data. The test data is subjected to the trained decision tree to obtain the classified data. . The experimental analysis are conducted for evaluating the performance of the proposed approach. The vehicle dataset and segment dataset from the UCI data repository is selected for the performance analysis. The results from the experimental analysis showed that the adaptive method has achieved a maximum average accuracy of 0.997 while the existing approach achieved only 0.985.

Keywords: Uncertain Data, Probability Distribution, Decision Tree Algorithm, Classification.

by B. Manjurathi, R. HariKumar, P. Nirmal Kumar

Abstract - System on chip is challenging, for both design and testing engineers due to its increase in power consumption. In test mode, the volume of the test data is extremely high when compared to normal mode, the switching activity which takes place between the test data thereby increases power consumption. The proposed technique includes hamming distance reordering of original test data, Column wise bit filling, difference vector technique and multiple run length code technique. It improves the compression ratio and reduces the average power and peak power of MINTEST test sets of ISCAS�89 benchmark circuits.

Keywords: CBF, Don�t Care Bit Filling Technique, Hamming Distance Reordering, Multiple Run Length Code Technique.

by A. Akilandeswari

Abstract - The merits of Discrete Wavelet Transform (DWT) over other traditional transforms have led to tremendous focus on the applications of Wavelet Transforms. This leads to the requirement for developing a hardware efficient VLSI implementation of the DWT along with low power consumption. In this paper, DWT architecture based on lifting scheme is considered with suitable modifications to the architecture by adopting low power techniques, which contributes to hardware efficient and low power implementation. A shift-add multiplier with signed floating point consideration is used to construct a 2D-DWT lifting based architecture. This proposed multiplier eliminates the use of power consuming costly conventional multipliers. The proposed multiplier represents the signed floating point results which is the drawback of representation in Verilog implementation .A Verilog model is designed and synthesized for the proposed architecture. The synthesis results indicate that Xilinx vertix-4 FPGA implementation of the proposed technique is demonstrated which achieves Multiplier less structure, 12% reduced number of slices and 10% reduced power architecture compared with conventional one level lifting 2D-DWT architecture using 9/7 Filter.

Keywords: DWT, Lifting Scheme, Signed Floating Point Shift-Add Multiplier, VLSI, Xilinx .