Latest issue

by N. Bahri, I. Werda, T. Grandpierre, M. Ben Ayed, N. Masmoudi, M. Akil

Vol. 8. n. 9, pp. 2025-2035

Abstract - Real-time H.264/AVC high definition video encoding represents a challenging workload to most existing programmable processors. The new technologies of programmable processors such as Graphic Processor Unit (GPU) and multicore Digital signal Processor (DSP) offer a very promising solution to overcome these constraints. In this paper, an optimized implementation of H264/AVC video encoder on a single core among the six cores of TMS320C6472 DSP for Common Intermediate Format (CIF) (352x288) resolution is presented in order to move afterwards to a multicore implementation for standard and high definitions (SD,HD). Algorithmic optimization is applied to the intra prediction module to reduce the computational time. Furthermore, based on the DSP architectural features, various structural and hardware optimizations are adopted to minimize external memory access. The parallelism between CPU processing and data transfers is fully exploited using an Enhanced Direct Memory Access controller (EDMA). Experimental results show that the whole proposed optimizations, on a single core running at 700 MHz for CIF resolution, improve the encoding speed by up to 42.91%. They allow reaching the real-time encoding 25 f/s without inducing any Peak Signal to Noise Ratio (PSNR) degradation or bit-rate increase and make possible to achieve real time implementation for SD and HD resolutions when exploiting multicore features.

Keywords: H264/AVC Encoder, TMS320C6472 DSP, Algorithmic and Structural Optimizations, EDMA, Real Time.

by H. Asaidi, A. Aarab, M. Bellouki

Vol. 8. n. 9, pp. 2036-2043

Abstract - Detecting moving objects by using an adaptive background model is a critical component for many vision-based applications. In this paper, we propose a novel background modeling algorithm for traffic video surveillance based on objects dynamicity. Our algorithm is based on the definition of background as a set of static objects and on the observation that a static object is a set of pixels having the same appearance in long time interval. The normalized RGB color is used to describe the appearance under change lighting conditions, and a binary label is introduced to capture the dynamicity of objects over time. Finally, the cardinality function, calculated in each continued time period, is used to select persistent appearance corresponding to background objects. An update formula is proposed to reconstruct background model periodically based on current frame and last background image. Experimental results from several video sequences validate the effectiveness of the proposed algorithm.

Keywords: Background Modeling, Background Subtraction, Background Update, Vehicle Detection, Visual Video Surveillance.

by Ezhilarasi P., Nirmalkumar P.

Vol. 8. n. 9, pp. 2044-2050

Abstract - Image retargeting is a vital requirement for the CMOS image sensor based applications at the user end. Many of the existing techniques are basically content based image retargeting which has high computational complexity and is not suitable for CMOS image sensors. For avoiding this practical hurdles, we addresses the increasing demand of visual signal delivery to terminals with arbitrary resolutions without heavy computational burden to the receiving end by incorporating the principle of seam carving into a wavelet codec. For each input image, block-based seam energy map is generated in the pixel domain and the integer wavelet transform is performed on the retargeted image. Unlike the conventional wavelet-based coding schemes, IWT coefficients are grouped and encoded according to the resultant seam energy map and bit streams are transmitted in energy descending order. In decoder, the end user has the ultimate choice for the spatial scalability without the need to examine the visual content and the image with arbitrary aspect ratio can be reconstructed.

Keywords: Integer Wavelet Transform, Reverse lifting, Seam Carving, SPIHT.

by K. S. Angel Viji, J. Jayakumari

Vol. 8. n. 9, pp. 2051-2057

Abstract - Image Segmentation is an important and a challenging aspect in the field of image processing. The most widely used image segmentation algorithm is region- based method that purely depends on the homogeneity of image intensities. But this method fails in accurate segmentation because of intensity in-homogeneity present in medical images. A texture is a set of parameters calculated in image processing in order to quantify the perceived texture of an image. The spatial arrangement of color or intensities in an image or selected region of an image is obtained from its texture. Hence a texture based region growing algorithm was proposed, by incorporating the advantages of the region growing and textural segmentation. Two innovative modified region growing algorithms have been proposed. In the first modification: texture and intensity based modified region growing (TIBMRG), the decision of growing the region into next pixel is based on pixel intensity and texture image (texture image is obtained by doing the LBP operator over the image). In the second modification: texture, intensity and orientation based modified region growing (TIOBMRG), the decision about assigning segment label is based on pixel intensity, orientation (which is obtained by applying gradient operator to the image) and texture image. We have examined the superiority of the proposed method and demonstrated it through a large number of experiments using MR images. Experimental results depict that the proposed methods are more flexible, robust and accurate than other popularly used algorithms.

Keywords: Clustering, Segmentation, Texture, MRI Image.

by Haider Ismael Shahadi, Razali Jidin, Wong Hung Way

Vol. 8. n. 9, pp. 2058-2067

Abstract - Discrete Wavelet Transform (DWT) becomes a major part for many applications. Fast, low area, and low power consumption hardware for DWT is necessary for some new technologies such as OFDM transceiver and wireless multimedia sensor networks. This paper presents efficient dual mode (decomposition and reconstruction) Integer Haar Lifting Wavelet Transform (IHLWT) architecture. The proposed architecture reduces the hardware requirements by exploiting the arithmetic operations redundancy which is involved in IHLWT computations. It is multiplier-free and it performs IHLWT with only a single adder and subtractor which have reconfigurable input buses to perform decomposition and reconstruction transformations. IEEE standard VHDL has been used to develop the proposed processor. This makes the design vendor independent and therefore easily portable across FPGA devices from multiple vendors. The generic design is flexible and can perform any arbitrary signal length. The synthesis of the processor showed that it requires low number of CLB-slices and low power consumption with high operating-frequency for various Xilinx FPGA devices. The processor has been successfully implemented and tested on Xilinx Spartan6-SP601 Evaluation Board. The implemented hardware has been tested in real time by using many recording audio signals. All the implemented hardware results were identical 100% with IHLWT software results.

Keywords: Lifting Wavelet Transform (LWT), Field Programmable Gate Array (FPGA), Haar Filter, Integer to Integer (Int2Int) Wavelet, Dual Mode Processor.

by E. Praynlin, P. Latha

Vol. 8. n. 9, pp. 2068-2074

Abstract - Project planning is one of the important aspects in software industry. Poor planning leads to failure of the project or delayed completion of the project. Projects mainly depend on effort which is estimated before the starting of the project. For developing a software human effort plays a significant role because the cost spent in infrastructure for developing software is very low or negligible compared to the human effort. Cost overrun, schedule overrun occur in the software development because of the wrong estimate made during the initial stage of software development. So proper estimation is essential for successful completion of software development. Several estimation techniques are available to estimate the effort in which neural network based estimation method play a prominent role. Minimal Resource Allocation Network (MRAN) a new type of network can be used to estimate the effort. To interpret the results MRAN is compared with conventional Back propagation network. To control better the time, cost and resource assigned to software project, organization need proper estimate of their size even before the project actually start.

Keywords: MRAN Network, Mean Magnitude of Relative Error (MMRE), Back Propagation Algorithm, Estimation.

by R. Latha, S. Senthil Kumar

Vol. 8. n. 9, pp. 2075-2079

Abstract - This paper deals with image processing algorithm that gives a solution for the problem of medical image segmentation. Biological neural network is an artificial abstract model of different parts of the brain or nervous system, featuring essential properties of the system using biologically realistic models. Artificial Neura Networks (ANNs) have been developed for a wide range of applications such as image analysis, enhancement, segmentation, feature extraction and pattern recognition. Among these, image segmentation is more important as it is widely used for object recognition. The watershed transformation algorithm used for segmentation of blood vessels from angiographic images results in some faulty segmented pixels while the use of neural network technique increases the performance with occurrence of some errors. Artificial neural network results in improved quality of image segmentation reflecting the mean square error to be minimum. The proposed algorithm using the combination of morphological filters and back propagation neural network are used for extracting blood vessels from angiographic images of human heart as they have linear structure and Gaussian like profile. Results on various medical data from a set of normal patients are presented and show that this algorithm can be used as a robust segmentation tool.

Keywords: Angiocardiography, Artificial Neural Network, Back Propagation, Image Segmentation, Morphological Operations.

by Thendral T., Vijaya MS., Karpagavalli S.

Vol. 8. n. 9, pp. 2080-2087

Abstract - Writer identification is the process of identifying the individual based on their handwriting. Handwriting exhibits behavioral characteristics of an individual and has been considered as unique. The style and shape of the letters written vary slightly for same writer and entirely different for different writers. Also alphabets in the handwritten text may have loops, crossings, junctions, different directions etc. Hence accurate prediction of individual based on his/her handwriting is highly complex and challenging task. This paper proposes a new model for discovering the writer’s identity based on Tamil handwriting. Writer identification problem is formulated as classification task and a pattern classification technique namely Support Vector Machine has been employed to construct the model. It has been reported about 93.8% of prediction accuracy by RBF kernel based classification model.

Keywords: Classification, Feature Extraction, Support Vector Machine, Training, Writer Identification.

by A. Velayudham, R. Kanthavel

Vol. 8. n. 9, pp. 2088-2101

Abstract - Computed tomography (CT) images are generally corrupted by several noises from the measurement process complicating the automatic feature extraction and analysis of clinical data. To achieve the best possible diagnosis it is important that medical images be sharp, clear, and free of noise and artifacts. While the technologies for acquiring digital medical images continue to improve, resulting in images of higher and higher resolution and quality, noise remains an issue for many medical images. Removing noise in these digital images remains one of the major challenges in the study of medical imaging. A variety of literatures have been developed to solve the problem of medical images denoising which is a significant stage in an automatic diagnosis system. In this paper, we propose a new image denoising technique using EMD and Dual Tree Complex Wavelet Packets. Here, histon process is used in order to overcome the smoothing filter type and it will not affect the lower dimensions. We have used two noises, like as Gaussian and salt & pepper for the proposed technique. The performance of the proposed image denoising technique is evaluated on the five CT images using the PSNR and SDME. For comparison analysis, our proposed denoising technique is compared with the existing work in various noise levels. From the results, we can conclude that the proposed denoising technique has shown the SDME of 48.33 but the existing technique show the PSNR of 39.84 for salt & pepper noise.

Keywords: Denoising, CT, EMD, Dual Tree Complex Wavelet Packet (DTCWP), PSNR, SDME.

by K. Madhan Kumar, R. Kanthavel

Vol. 8. n. 9, pp. 2102-2112

Abstract - To many geographic systems (GIS) application scheme such as urban planning and navigation, updating road network database is critical problem. Rapidly changing urban environments accelerate the need for frequent updates or revisions of road network databases. With the advent of high-resolution satellite images, there has been a resurgence of research interest in road extraction techniques. However, due to the extreme complexity of an urban scene, automatic road network extraction continues to be challenging research topic. In this paper, we have proposed a road map extraction system with two efficient filters using satellite images. Here, Unscented Kalman filter (UKF) is used in combination with Gauss-Hermite Kalman Filter (GHKF) to trace and identify various connected road paths and to avoid obstacles under diverse conditions. Unscented Kalman filter (UKF) component is responsible for tracing axis coordinates of a road region until it comes to a severe obstacle or an intersection. Then, the Gauss-Hermite Kalman Filter (GHKF) module takes the control of the road extraction process and regains track of the road or possibly road branches on the other side of a road junction or obstacle. From the results, we ensure that the proposed road extraction technique outperformed the existing approach by achieving the accuracy of 98.452% in cluster 10.

Keywords: Unscented Kalman Filter (UKF) and Gauss-Hermite Kalman Filter (GHKF), Satellite Image, Road Extraction.

by Noridayu Manshor, Amir Rizaan Abdul Rahiman, Raja Azlina Raja Mahmood

Vol. 8. n. 9, pp. 2113-2117

Abstract - In computer vision, objects in an image can be described using many features such as shape, color, texture and local features. The number of dimensions for each type of feature has differing size. Basically, the underlying belief from a recognition point of view is that, the more features being used, the better the recognition performance. However, having more features does not necessarily correlate to better performance. The higher dimensional vectors resulting from fusion might contain irrelevant or noisy features that can degrade classifier performance. Repetitive and potentially useless information might be present which further escalates the ‘curse of dimensionality’ problem. Consequently, unwanted and irrelevant features are removed from the combination of features. Although this technique provides promising recognition performance, it is not efficient when it comes to computational time in model building. This study proposes meta-classifier framework to ensure all relevant features are not ignored, while maintaining minimal computational time. In this framework, individual classifiers are trained using the local and global shape features, respectively. Then, these classifiers results are combined as input to the meta-classifier. Experimental results have shown to be comparable, or superior to existing state-of- the-art works for object class recognition.

Keywords: Meta-Classifier, Shape Features, Local Features, Fusion.

by U. Latha, K. Ramesh Kumar

Vol. 8. n. 9, pp. 2118-2122

Abstract - The Biometric data is subject to on-going changes and create a crucial problem in fingerprint database. To deal with this, a security protocol is proposed to protect the finger prints information from the prohibited users. Here, a security protocol is proposed to protect the finger prints information. The proposed system comprised of three phases namely, fingerprint reconstruction, feature extraction and development of trigon based security protocol. In fingerprint reconstruction, the different crack variance level finger prints images are reconstructed by the M-band Dual Tree Complex Wavelet Transform (DTCWT). After that, the reconstructed fingerprint image features are stored in the database and this extracted fingerprint features information is protected by our proposed security protocol. A set of finger print images are utilized to evaluate the performance of security protocol and the result from this process guarantees the healthiness of the proposed trigon based security protocol. The implementation results show the effectiveness of proposed trigon based security protocol in protecting the finger print information and the achieved improvement in image reconstruction and the security process.

Keywords: 2D Dual Tree Complex Wavelet Transform (DTCWT), Trigon Based Security Protocol, Authentication Server, Backend Server, Authentication Key.

by M’hamed Rahmouni, Samir Mbarki

Vol. 8. n. 9, pp. 2123-2135

Abstract - Code generation isn't a new concept. It's been around for a while and has been gaining popularity with the model-driven development (MDD) movement as a way to increase productivity. In this paper, we experience a high level technique method based on JET2 to generate the code of an e-commerce web application which is a PC online shopping. This technique is based on the combination of the UML class diagram and the UML activity diagram. In the algorithm of transformation, we consider only the operations belong to the two diagrams already combined. Practically, we transform only the operations that have an activity diagram and belong to the class diagram. In this technique method, we begin by written the transformation rules in ATL transformation language in order to generate the MVC2 web model. In The second step, we use the generated model as an input file of JET2 to generate the code of the e-commerce web application. Also, it presents a case study to illustrate this proposal.

Keywords: MDA, ATL Transformation, MVC2 Web, E-Commerce, PIM, PSM, Metamodel, Code Generation.

by M. Ramalingam, R. M. S. Parvathi

Vol. 8. n. 9, pp. 2136-2141

Abstract - Semantic Access Control model is based on the semantic properties of the resources to be controlled, properties of the clients that request access to them, semantics about the context and lastly, semantics about the attribute certificates trusted by the access control system. Many algorithms and techniques have been developed for the Semantic Access Control. Our previous work introduced a fine grained access control mechanism for accessing semantic web services. However, the policy based semantic access control method does not have any security in the user contribution i.e., the request user may be valid user or invalid user. So, there is more chance for the invalid users to get continuous access to the web services and also they hacks more information from the web services. Thus, such drawbacks in our previous work need to be reduced for attaining a high-quality performance in semantic web services. Hence, a security model is proposed in this paper to include in the security framework to investigate the validity of users. The valid number of users are registered and stored in the registration service. If new user request is obtained, the user’s request query and the registration service are processed to find whether this requested user is a valid or invalid user. The simulation results show that the secure semantic aware fine grained web services accessing framework is robust against the accessing of invalid users without any compromise in the performance of the semantic module.

Keywords: Security, Semantic, Fine-Grained, Web Services, User, Service Providers, Access.

by C. Kamalanathan, S. Valarmathy, S. Kirubakaran

Vol. 8. n. 9, pp. 2142-2149

Abstract - The main objective of this research work is to design and develop a Fuzzy Logic based trust and reputation model for secure resource allocation in cloud computing. The cloud computing is one of the major topics discussed among the IT professionals in recent days. In this paper trust manager and reputation manager based approach is used to update the security. Initially, the user access a resource block through the scheduling manager and a form will send to the user after accessing the resource block to fill the attribute values of trust factor and reputation factor. The trust factor and reputation value is then calculated for the resource center and it is given to the Fuzzy Logic system to get the security score of a resource center. The benefit of our proposed technique is to provide the security controls in accessing the cloud resources from cloud computing due to various security issues happened in networks, databases, resource scheduling, transaction management and load balancing.

Keywords: Trust Factor, Reputation Factor, Fuzzy Logic System, Security Score, Resource Center.

by K. G. Parthiban, S. Vijayachitra

Vol. 8. n. 9, pp. 2150-2159

Abstract - Studying the behavior of spikes in EEG is important for detecting brain abnormality. In EEG recorded signal contain large amount of spikes, so the spike detection is a technical challenge one. Morphological filters are normally used to separate this spikes from the recorded EEG signal. In existing technique the Gaussian function is used in morphological filter to find out the optimal structuring element. Using this function, it cannot find the accurate optimal structuring element, for that we have intended to propose a spike detection method using morphological filter with optimization technique. In the proposed method, initially the EEG signals noise is removed by the wavelet technique and this preprocessed EEG signals are given to the spike detection process. Morphological filter is used for the spike detection, in which optimal structuring elements are computed by the hybrid optimization technique as GA-PSO. After that, an amplitude threshold should be set to detect the occurrence of individual spikes. Hence, the spikes can be detected more effectively by achieving more number of correctly detected spikes rather than the conventional spike detection algorithms. Moreover our proposed technique performance is compared with the PSO and GA optimization methods.

Keywords: Spike Detection, Morphological Filter, Haar Wavelet, Genetic Algorithm (GA), Particle Swarm Optimization (PSO).

by L. Bagdadi, B. Messabih

Vol. 8. n. 9, pp. 2160-2167

Abstract - In recent years, the injection attacks are the most common application layer attacks currently being used on the Internet. The growing acceptance of XML technologies for documents and protocols make the web application uncovered and exploited by hackers. XPath is a language used for querying XML document. XPath Injection attacks occur when a web site uses user-supplied information to construct an XPath query for XML data. In this paper, we proposed an SVM learning based approach to protect web services against the XPath injection attacks. We have implemented a kernel based on trees and incorporate it to the libSVM tool. To proceed, we extract all possible sub trees from the xpath parse tree request, then we find the similarity between two structures by summing the similarity of their substructures. The architecture of our proposed solution is compounded of two principals modules : the learning engine and the predictor one. Before a treatment of incoming XPath queries, an Aspect oriented Programming interceptor component is invoked to intercept this query and submit it to the SVM engine predictor.

Keywords: XPath Injection Attacks, Intrusion Detection, Security in Web Services, Aspect Oriented Programming, SVM.

by D. Karthika Renuka, P. Visalakshi

Vol. 8. n. 9, pp. 2168-2177

Abstract - Email spam is a serious worldwide problem which causes problems for almost all computer users. This issue not only affects normal users of the internet, but also causes a huge problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting user’s time and network bandwidth. Recently, various researchers are presented several email spam classification techniques. In this paper, we have developed an efficient technique to classify the email spam using firefly and naïve bayes classifier. Initially, the input email data is given to the feature selection to select the suitable feature for spam classification. The traditional firefly algorithm is taken and the optimized feature space is chosen with the best fitness. Once the best feature space is identified through firefly algorithm, the spam classification is done using the naïve bayes classifier. The results for the spam detection are validated through evaluation metrics namely, sensitivity, specificity, accuracy. For comparative analysis, proposed spam classification is compared with the existing works such as particle swarm optimization and neural network. From the results, our proposed algorithm performed better than PSO algorithm and neural network in terms of accuracy and specificity.

Keywords: Spam, Feature Selection, Firefly, Classification, Naïve-Bayes.

by Mohd Hanif Ahmad, Aida Mustapha, Nazli Mohd Khairudin

Vol. 8. n. 9, pp. 2178-2186

Abstract - Web usage mining enables organizations and website owners to study the user access patterns or behaviors when navigating their websites. In predicting user navigation patterns, previous study has proposed a two-stage clustering-and-classification of web log data. The main issues in web usage mining is the precision of recommendations in user navigation patterns since it’s will affect the quality of prediction of user future navigation. The quality and precision of navigation patterns produce in the clustering stage is useful contribution in designing an accurate user prediction system. This paper aims to undertake comparative analysis on two clustering algorithms, which are graph partitioning and agglomerative hierarchical clustering to compare user navigation patterns produced by each clustering technique. The results from clustering experiments showed that graph partitioning algorithm produced a detailed list of navigation patterns compared to agglomerative hierarchical clustering.

Keywords: Web Usage Mining, Clustering, Graph Partitioning, Agglomerative Hierarchical Clustering.

by Chitra A., Nageswara Guptha M.

Vol. 8. n. 9, pp. 2187-2196

Abstract - The current and future business processes would be service oriented. The required service is selected using discovery process. The discovery process uses in-memory parse tree construction or callback based document parsing. Existing service discovery systems stores both functional attributed and QoS attributed in tree data structures and they are time consuming, space consuming, requires frequent parse tree construction in various location, difficult to replicate and distribute parse tree and replicated parse trees are inconsistent. This paper proposes an efficient methodology to improve the performance of in memory parse tree discovery process over extended web service architecture. The system uses two parallel discovery algorithms, Synchronous and Asynchronous algorithms for service discovery, both algorithms uses the tree and indexed data structure for service discovery and the later also uses a bitmap table to verify functional requirements. The services are selected and ranked based on user request using Coefficient of Variance. The proposed algorithms are detailed in this paper. The experimental results are conducted on benchmark dataset are discussed. The proposed system ensures availability, consistency, effective space utilization, eases replication and distribution and improves response time for service discovery by 30%.

Keywords: Business Intelligence, Service Discovery, Service Oriented Computing, Service Registry, XML Parse Tree.

by M. Priyadharshini, R. Baskaran, N. Balaji, M. S. Saleem Basha

Vol. 8. n. 9, pp. 2197-2204

Abstract - Cloud Computing is found to be today’s most commonly used Service Oriented Architecture (SOA) implementation. Cloud utilizes XML-based technologies like Web Services for accessing and controlling the cloud, these are of particular importance for the security assessment of cloud systems. XML usage in Web Service introduces various vulnerabilities which affects basic security factors such as Confidentiality, Integrity and Availability. Various frameworks aiming at countering the XML based attacks were designed and developed. The Analysis of the frameworks available for countering the XML-based attacks simulated in the SOAP messages is presented benefiting the future researchers and also provides insight of various attack simulations and the countermeasures respectively. The parameters responsible for evaluating the strength of the frameworks were also specified and discussed as part of this work.

Keywords: Web Services Security, XML Security, SOAP, Cloud Computing, Service Oriented Architecture.

by M. R. Ramli, B. Hussin, N. K. Ibrahim

Vol. 8. n. 9, pp. 2205-2212

Abstract - Employee schedule is a list showing the arrangement such as dates and times of each employee must work at a particular period of time. Employee scheduling is one of the important tasks need to be concerned as it influences the organizational productivity of the complex tasks among employees. Common issues in healthcare systems worldwide specifically in employee scheduling are the unfairness of the working shifts between nurses and the shortages of nursing staffs combined with the uncertain nature of patient workloads. Assigning each available nurse to the right place at the right time is therefore a major concern among many healthcare organizations. A well-designed schedule algorithm shall be able to generate an efficient work task that can precede restriction and variability. Nevertheless, the fairness of the task been assigned to the nurses should also be considered from their perspectives. This journal discusses the entire nurse scheduling problem as well as methods with optimizing techniques and efficient solution algorithms used to address the problem with fairness as the key objective function. The result from the simulated data represents how the tasks are being assigned fairly among nurses. Detailed discussion of these aspects shall be provided in the main body of the paper.

Keywords: Optimisation Technique, Heuristic, Scheduling, Fitness.

by Kayarvizhy N., Kanmani S.

Vol. 8. n. 9, pp. 2213-2220

Abstract - Object oriented programming has taken a major role in the development of large, complex systems. Software quality of these systems is of prime importance to many stakeholders. One approach is to use object oriented metrics to predict and control the quality of these systems. Many metrics have been proposed in the literature to monitor specific object oriented traits. However due to the sheer numbers of metrics available it becomes a difficult task to pick and choose relevant metrics for applying to specific needs. This motivated us to analyze the object oriented metrics that have been proposed and group them based on various factors. Our analysis would help developers and managers to pick the right choice of metrics for their need. It will also help researchers of object oriented metrics to identify potential areas which they could focus upon.

Keywords: Object Oriented Metrics, Software Quality, Cohesion, Coupling, Inheritance, Polymorphism, Data Encapsulation, Data Abstraction.

by C. Siva, S. Arumugam

Vol. 8. n. 9, pp. 2221-2228

Abstract - This paper deals with the Fast Reroute technique which is the extension of the Route Reliability Testing (RRT) algorithm. The main issues of the BGP are link failure and untrustworthiness of the link and many more. In a network when the actual link fails the data will not be sent to its destination and which results in congestion and also increases the load of the network. To deal with these issues, in this paper we propose a Fast ReRoute (FRR) Technique in BGP, which helps when the primary link fails by finding an alternate link for the data. The main advantage of this of this proposal is while finding the alternate link the lowest post failure, traffic load across all the links into the account and also it is possible to decrease the data packet loss. By simulation results, we show that the proposed rerouting technique reduces the packet loss and increases the throughput.

Keywords: Route Reliability Testing (RRT), Border Gateway Protocol (BGP), Fast ReRoute (FRR) Technique.

by R. Velumani, K. Duraiswamy

Vol. 8. n. 9, pp. 2229-2238

Abstract - Designing of a multicast routing protocol for MANETs faces several challenges. Among them Group membership management, the characteristics like scalable and robustness poses several cumbersome problem in extracting better performance of multicast protocols. Here we propose a MSRDMP protocol for mobile ad hoc networks by which reliability and robustness is achieved through mutual sharing of works among the members of the group. In this approach mutual alert message is created using a signal to noise ratio and global position system which result in efficient group membership management. Datagram packet is appended with the distance with which Group leader is connected with virtual reference point. We enhanced CSMA/CA mechanism to recover the lost data. The node which has lost the data packet invokes the interim CTS request. Leadership Track Node (LTN) is one among the multicast group which takes care of lost packet on responding interim CTS request. The simulation result compared to existing protocols RSGM and ODMRP shows that optimal control overhead, high degree of packet delivery ratio and moderately constant end-to-end delay and average path length under varying moving speed ,group size and number of groups.

Keywords: Interim CTS, GPS, MANETS, Multicast Routing, Leadership Track Node, MSRDMP.

by P. S. Velumani, S. Murugappan

Vol. 8. n. 9, pp. 2239-2246

Abstract - In wireless sensor networks (WSN), the secure localization is extremely significant for enhancing the accuracy of localization and robustness against security threats. Also most the existing literature works lacks the security approach towards localization mechanism which may affect the secure communication of the data. Hence in this paper, we propose a secure authentication technique for localization in WSN. Initially the position of sensor nodes is estimated using proximity distance map computation. The anchor nodes then generate a location based key pair for each sensor node. This ensures that the attackers cannot exploit the positions and location based keys of compromised nodes. Then the mutual authentication of neighbor nodes is performed based on location information. By simulation results we show that the proposed approach offers improved security in WSN.

Keywords: Wireless Sensor Networks (WSN), Sensor Nodes, Anchor Nodes.

by Sasikala E., N. Rengarajan

Vol. 8. n. 9, pp. 2247-2253

Abstract - Jamming attack is one of the most serious threat in wireless sensor networks (WSN). This type of attack not only blocks the ongoing communication but also exhaust the energy of the sensor nodes. In WSN, several types of DoS attacks in different layers might be performed. The physical layer being the lowest layer and the first to be attacked by jammers. The mechanisms to prevent jamming attacks include payment for network resources, pushback, strong authentication and identification of traffic. Therefore guarding against DoS attacks is a critical component of any security system but there is lack of research for preventing such attacks. In this paper, the physical layer DoS attack is analyzed and a defense mechanism is proposed using modified ant system. The proposed detection and defense mechanism is simulated using Matlab 6.5. The simulation results show that the proposed scheme helps in achieving maximum reliability on DoS claims improving the Quality of Service (QoS) of WSN.

Keywords: Wireless Sensor Network, Denial of Service (DoS), Jamming Attack, Detection Theory, Ant System.

by Igor V. Zotov, Ruslan V. Bredikhin, Evgeni A. Titenko

Vol. 8. n. 9, pp. 2254-2261

Abstract - In the article, the authors present a new distributed hardware-level method for barrier synchronization of parallel programs in a mesh-connected multicomputer, which is based on the usage of a virtual multislice pipeline coordinating environment timed by clock pulse waves.

Keywords: Mesh-Connected Multicomputer, Parallel Processes, Barrier Synchronization, Hardware-Level Barrier.

by P. Srinivasan, P. Kamalakkannan, S. P. Shantha Rajah

Vol. 8. n. 9, pp. 2262-2268

Abstract - Mobile ad hoc networks (MANETs) are very promising wireless technology and they support wide range of applications. Due to the dynamic nature of MANETS, their routing process faces several challenges. In this paper, we propose a new, Load and Stability Aware Ad Hoc on-demand Distance Vector protocol (LSA-AODV) for efficient data transmission in MANET. It applies fuzzy logic to stability metric and traffic density of the route. The ultimate intend is to select a reliable path for data communication and to reduce the number of route breakages, collisions and contention. . It also utilizes stability and traffic density metrics to control broadcasting of routing messages. This protocol is compared with other similar routing protocols: LBAR and AODV. We use ns-2 for simulation. Our simulation results show that, the proposed protocol outperforms the existing routing protocols in terms of end-to-end delay and packet delivery ratio. It also considerably reduced the routing overhead.

Keywords: Mobile Ad-Hoc Network, Stability, Load Aware, Fuzzy Applicability, Routing.

by Marwan Al-Jemeli, Fawnizu A. Hussin

Vol. 8. n. 9, pp. 2269-2277

Abstract - Increasing the life span and system throughput in wireless sensor networks (WSN) are vital objectives because of the energy limitations in WSNs. Routing protocols are implemented to balance the traffic flow between the nodes in the network when a source node requests to send data to a destination. The optimal route is chosen by the routing protocol using specific rules and parameters, i.e. routing metrics. This paper presents a combination of route choice metrics aimed for mobile WSN applications. The combined metric is based on two hardware based information, the received signal strength indicator (RSSI) and the neighbor node residual energy. Simulation results show that the proposed metric improves the network performance in terms of energy budget and increases the system throughput by up to 27% over the hop count metric and 37% over Signal-to-noise ratio (SNR) and Expected Transmission count (ETX) metrics in a mobile wireless sensor network environment.

Keywords: Wireless Sensor Networks (WSN), Routing Protocol, Routing Metric, Energy, Received Signal Strength Indicator (RSSI), Ad Hoc On-Demand Distance Vector (AODV).

by P. Manikandan, S. Selvarajan

Vol. 8. n. 9, pp. 2278-2287

Abstract - Data clustering as one of the important data mining techniques is a fundamental and widely used method to achieve useful information about data. The purpose of clustering is to group together data points, which are close to one another. In face of the clustering problem, clustering methods still suffer from trapping in a local optimum and cannot often find global clusters. In order to overcome the shortcoming of the available clustering methods, this paper presents a hybrid clustering algorithm. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Cuckoo Search (CS), called CS-PSO, for optimally clustering N object into K clusters. The new CS-PSO algorithm is tested on several data sets, and its performance is compared with those of GA, FCM, Fuzzy-PSO and K-means clustering. The simulation results show that the new method carries out better results than the Genetic algorithm (GA), K-means, Fuzzy C-means FCM), Genetic-K means and Fuzzy-PSO.

Keywords: PSO, CS, GA, FCM, K-Means, Hybrid Clustering, Optimization .

by V. R. Balaji, S. Subramanian

Vol. 8. n. 9, pp. 2288-2295

Abstract - The speech enhancement plays a vital role commonly used in noisy environment to develop the performance of speech identification in mobile phones or in car navigation system. Thus the quality of the performance of the speech recognition is becoming worse due to the presence of noises in the surrounding. The objective is to increase the evident quality of the speech and to develop the transparency. Signal representation and enhancement in cosine transformation is observed to provide significant results. As an alternative of using DCT, a combination of conventional Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) which forms the another transform called as the Discrete Fractional Cosine Transform (DFrCT). The DFrCT have a free parameter, its fraction. In order to deal with the issue of frame to frame deviations of the Cosine Transformations, DFrCT is integrated with Pitch Synchronous Analysis (PSA). Also Pitch Synchronous OverLap and Add (PSOLA) method are used to enhance the performance of PSA. Moreover, in order to improve the noise minimization of the system, Improved Iterative Wiener Filtering approach called Adaptive Kalman Filter Combined with Perceptual Weighting Filter is used in this approach. This filter is used to eliminate the matrix operations, reduces both the calculation time and complexity. Thus, a novel DFrCT based speech enhancement using improved iterative filtering algorithm integrated with PSA is used in this approach.

Keywords: Improved Iterative Wiener Filtering, Advanced Discrete Cosine Transform, Pitch Synchronous Analysis, Perceptual Evaluation of Speech Quality.

by J. Sangeetha, S. Jothilakshmi

Vol. 8. n. 9, pp. 2296-2302

Abstract - In this paper, propose a method for translating text from English to Tamil which is one of the Dravidian languages. Rule based machine translation technique is used here, which involves the formation of rules which helps in re-ordering of the syntactic structures of the source language sentence along with its dependency information which bring that close to the structure of the target sentence. The parser identifies the syntactical elements in English sentences and suggests its Dravidian language translation taking into account various grammatical forms of those Dravidian languages. The usage of the parser in developing the syntactic structure plays a major role in the translation process. There are mainly two types of rules used here, one is transfer link rule and the other is morphological rules. In this method, the transfer link rules are used for generating target structure. Morphological rules are used for assigning morphological features. Context Free Grammars (CFG) is used in generation of the language structures. By using this approach, given English text can be translated to its Tamil equivalent.

Keywords: Natural Language Processing, Machine Translation, Reordering, Parsing, Morphological Analysis, Lexicalization; Transliteration.

by S. Revathy, B. Parvathavarthini

Vol. 8. n. 9, pp. 2303-2308

Abstract - There are advantages to both fuzzy set and rough set theories, Combining these two and used for clustering gives better results. Rough clustering is less restrictive than hard clustering and less descriptive than fuzzy clustering. Rough clustering is an appropriate method since it separates the objects that are definite members of a cluster from the objects that are only possible members of a cluster. In fuzzy clustering similarities are described by membership degrees while in rough clustering definite and possible members to a cluster are detected. Fuzzy Rough Correlation Factor is the threshold for degree of fuzziness. It determines how low a DFR value shall be for it to be considered for cluster membership assignment. This paper proposes new modified rough fuzzy clustering algorithm based on fuzzy rough correlation factor. Hence rough fuzzy clustering can be derived directly from the results obtained thro fuzzy clustering.

Keywords: Fuzzy Clustering, Fuzzy Rough Correlation Factor, Rough Fuzzy Clustering.