Trends in Computer Science, Engineering and Information Technology

This paper investigates the hybrid chaos synchronization of identical Liu systems, identical Lü systems, and non-identical Liu and Lü systems by active nonlinear control. Liu system (Liu

et al.

2004) and Lü system (Lü and Chen, 2002) are important models of three-dimensional chaotic systems. Hybrid synchronization of the three-dimensional chaotic systems considered in this paper are achieved through the synchronization of the first and last pairs of states and anti-synchronization of the middle pairs of the two systems. Sufficient conditions for hybrid synchronization of identical Liu, identical Lü, and non-identical Liu and Lü systems are derived using active nonlinear control and Lyapunov stability theory. Since the Lyapunov exponents are not needed for these calculations, the active nonlinear control is an effective and convenient method for the hybrid synchronization of the chaotic systems addressed in this paper. Numerical simulations are shown to illustrate the effectiveness of the proposed synchronization schemes.

Many Internet users make use of search engines to locate a web page or information they want. These search engines have become a fundamental tool in the World Wide Web. Since the capability of search engines to reach any part of the web has increased, security loopholes of any websites face the risk of being exposed globally. This poses serious security threats on the web. In this paper, we discuss various methods to prevent web server attacks through search engines.

Sea target detection from remote sensing imagery is very important, with a wide array of applications in areas such as fishery management, vessel traffic services, and naval warfare. This paper focuses on the issue of ship detection from spaceborne optical images (SDSOI). Although advantages of synthetic aperture radar (SAR) result in that most of current ship detection approaches are based on SAR images. But disadvantages of SAR still exist. Such as the limited number of SAR sensors, the relatively long revisit cycle, and the relatively lower resolution. To overcome these disadvantages a new classification algorithm using color and texture is introduced for Ship detection. Color information is computationally cheap to learn and process. However in many cases, color alone does not provide enough information for classification. Texture information also can improve classification performance. This algorithm uses both color and texture features. In this approach for the construction of a hybrid color-texture space we are using mutual information and three aspects: 1) Classifies ship candidates 2) The relevant classes are automatically built by the samples’ Appearances and 3) Shape and Texture features. Experimental results of SDSOI on a large image set captured by optical sensors from multiple satellites show that our approach is effective in distinguishing between ships and non ships, and obtains a satisfactory ship detection performance.. Feature extraction is done by the co-occurrence matrix with SVM (Support Vectors Machine) as a classifier. Therefore this algorithm may attain a very good classification rate.

Internet is only the medium which connects billions of people globally and finally has been removed all physical barriers. Bank is also one of the sector which greatly influenced with the invent of Internet. However, Internet banking is one of the service of the bank which provides many services online to the customers without being physically visit the bank.On the one end Internet banking bring many services to the customer but on the contrary also pose a risk of fraud, theft and vandalism.

The key aspect of any online services to authenticate the customer without any face-to-face communication.which ultimately challenge for the technology to validate them without any compromise. In the whole scenario security can be compromised at three level ;at the client,in transit and at the server.Presently there are number of authentications methods proposed by the researcher worldwide for the financial institutions but my study is particularly focus on Indian Banks. Moreover, a special focus given on online services providing by the banks to their customers. Study reveals the authentication methods is a layered approach to achieve high degree of privacy and security in online services and which is a combination of two approach such as mobile and Token based authentication methods.

I have conducted a study of 25 banks of India to collect the data regarding present Authentication methods adopted presently. The paper describe in detail working of the said authentication methods and future implementation of these technologies.

In recent years, many wavelet coders that use various spatially adaptive coding technique to compress the image. Level of flexibility and the coding efficiency are two crucial issues in spatially adaptive methods. So in this paper “spherical coder” is introduced. The objective of this paper is to combine the spherical tree with the wavelet lifting technique and compare the performance of the spherical tree between the different coding technique such as arithmetic, Huffman and run length. The Comparison is made by using PSNR and Compression Ratio (CR). It is shown the Spherical tree in wavelet lifting with the arithmetic coder gives high CR value.

The objective of this project is to use a novel method for generic visible watermarking with a capability of lossless image recovery. This method is based on one-to-one compound mapping algorithm. The compound mappings are proved to be reversible, which allows for lossless recovery of original images from watermarked images. The mappings may be adjusted to yield pixel values close to those of desired visible watermarks. Different types of visible watermarks, including opaque monochrome and translucent full color ones, are embedded as applications of the proposed generic approach. To increase the visual perception Discrete Wavelet Transforms is used.Experimental results show that our method provides a higher embedding capacity compared to the other algorithms proposed in the literature.

An important aspect of software testing today is the ability to seamlessly move from analysis models to generating test scenarios and test cases. Use Case Maps(UCMs) are a scenario-based software engineering technique used at early stages of software development. The notation is most suited to capturing and eliciting use cases as well as high-level architectural design and for test case generation. Test patterns provide established solutions for designing tests or for supporting the testing process. In this work, we define test patterns based on the the primitives of the Use Case Maps to aid the process of test scenario generation.

Currently information security is becoming more important in data storage and transmission. Images are broadly used in several processes. Therefore, the protection of image data from unauthorized access is essential. Image encryption plays a major role in the field of information hiding. The objective of this paper is to provide an efficient coding technique for compress the encrypted gray scale images without any loss. This is made possible by using Huffman coding. This paper uses resolution-progressive compression (RPC) technique to achieve better result. RPC compresses an encrypted image progressively in resolution, such that the decoder can observe a low-resolution version of the image, study local statistics based on it, and use the statistics to decode the next resolution level. The experimental results show that the method used in this paper for compression gives good result.

This paper investigates the global chaos synchronization of identical hyperchaotic Pang systems (Pang and Liu, 2011) and synchronization of non-identical hyperchaotic Pang system and Wang system (Wang and Liu, 2006). Active nonlinear feedback control is the method used to achieve the synchronization of the identical and different hyperchaotic Pang and Wang systems addressed in this paper and our results are established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active control method is effective and convenient to synchronize identical and different hyperchaotic Pang and Wang systems. Numerical simulations are given to illustrate the effectiveness of the proposed synchronization schemes for the global chaos synchronization of hyperchaotic systems addressed in this paper.

Web Mining is mining information from the Web. Web Usage Mining extracts user accessing patterns and gives the desired web page quickly. While accessing web, the user activities gets recorded in a web server log. Using data mining algorithm, if mined properly, the web log acts as the gateway to user’s interests. But, merely considering presence or absence of web page in web log does not gauge its importance to user. Hence, in this work two parameters namely

frequency of the access pattern

and

utility value

are used to find better access patterns of the user and succeeded. This paper uses the method of Frequent Pattern (FP) Tree generation to find frequency of access pattern and the time spent by user on each web page is considered as the utility value.

Inter-agent communication is one of the main concerns of agent oriented requirements engineering that is delineated as managing inter-dependencies and interaction among various agents performing collaborative activities. To carry out cooperative activities, tasks are disseminated and delegated to other agents with a rationale of sharing mutual expertise and potential. So an agent may be dependent on other agent for accomplishing a goal or for want of a resource to achieve that goal. This requires an agent to quantify the dependency needs termed as Degree of Dependency (DoD) to realize whether it should delegate the task to another agent, if yes, then to whom so that overall quality of Multi-Agent System (MAS) is not compromised. To evaluate DoD, this work employs domain knowledge that is an inclusive knowledge of an environment containing the business rules, credentials and reports to understand the business needs precisely. As the domain knowledge may be fuzzy and uncertain, Fuzzy Inference system is utilized to evaluate DoD from domain knowledge. This will assist the developer to address inter-agent coordination issues without squandering resources and hence in building MAS of high quality.

With the advent of internet the world has become very small in the arena of communication. The information can be communicated from one place to another place through the internet very easily and with great speed. Before communicating with each other the user must be authenticated by the remote system to provide security to the information. The concept of username with password is an efficient method of maintaining shared secret information between a user and a remote system. The information that is communicated through the internet passes across an unsecured network. This gives security breaches to the information of the user authentication and is being hacked by the hacker. Even though the user credentials are encrypted into a cipher text, still with the constant monitoring, the hacker is able to guess and decrypt the user credentials. Here, we are presenting some novel methods to prevent the hacker in knowing the user credentials even though it passes through the unsecured networks.

A web search engine searches for information in the World Wide Web. The user needs to search for information in various domains such as financial and healthcare. The user is expected to remember various Vertical Search Engine names to extract appropriate information. A Vertical Search Engine is a domain specific search engine that provides the user with results for specific queries on that domain. Often the user is unable to get the exact information from the web due to the different page ranking techniques followed by individual search engines. Meta Search Engines solve this problem to a certain level by sending the user’s search queries to various search engines and combining the search results. This paper proposes Multi Domain Meta Search Engines that facilitate efficient Information Retrieval on multiple domains. User interfaces for selecting domain, category and search engines is presented. An intelligent User Interface is also designed to get the user query and to send it to appropriate search engines. Few algorithms are designed to combine results from various search engines and also to display the results.

The advancements in communication software and hardware is increasing the amount of information in the form of text, image, audio and video being shared. This increase arise the issue of protecting the intellectual property. This paper studies the protection of video signals through watermarking. The system proposed uses a combination of visual cryptography and wavelets to create a nested watermark which is embedded into the raw uncompressed video signal using a geometric warping technology. To improve robustness against compression, the method embeds the watermark in relevant area using a block selection method. The experimental results prove that the proposed system is efficient in terms of capacity and robustness against various attacks.

Wireless mobile ad-hoc networks are characterized as networks without any physical connections. Multipath routing is a new extension to many traditional routing protocols in MANETs. Using multipath routing in MANETs can save energy, reduce frequent routing update, enhance data transmission rates, and increase wireless network bandwidth. There are numerous Multi-paths on-demand routing algorithms exist to discover several paths instead of one, once the routing is performed. In this paper a new Robust Fault Tolerant AOMDV (RFTA) algorithm is proposed to improve the fault tolerance and efficient routing in mobile adhoc network (MANET). The algorithm is compared with various multi path routing protocols like SMR, SMS and MDSR. The efficiency of the protocol has been evaluated on different scenarios using performance metrics such as packet delivery ratio and end-to-end delay.

Every crypto system is an interactive system, every interactive system is a formal system, the results of the formal system can be proved through the Godel’s incompleteness Theorem. Proving techniques in the solution of problems designed on formal systems employ logical connectivity. This logical programming is possible from the proof of Godel’s incompleteness theorem in the formal systems. Any intractable problems can be solved by heuristic approach. One such approach is the Godel’s attempt, we realize the automated steps translating the proof of Godel’s theorem as our zero knowledge interactive steps. The implementation of this approach is being illustrated.

Web 3.0 is an evolving extension of the current web environment. Information in web 3.0 can be collaborated and communicated when queried. Web 3.0 architecture provides an excellent learning experience to the students. Web 3.0 is 3 D, media centric and semantic. Web based learning has been on high in recent days. Web 3.0 has intelligent agents as tutors to collect and disseminate the answers to the queries by the students. Completely Interactive learner’s query determine the customization of the intelligent tutor. This paper analyses the Web 3.0 learning environment attributes.

Rule acquisition is a technique of data mining that is used to deduce inferences from large databases. These inferences cannot be noticed easily without data mining.

Genetic algorithms

(GAs) are considered as a global search approach for optimization problems. Through the proper evaluation strategy, the best “chromosome” can be found from the numerous genetic combinations. In the self-adaptive genetic algorithm, its main thought is to let control parameter (crossover rate, mutation rate) adjusted adaptively within the proper range, thus achieve a more optimum solution. It is proved that the self-adaptive genetic algorithm is with excellent convergence and higher precision than the traditional genetic algorithm.

Resource allocation and management in the Grid environment is a complex undertaking as resources are distributed and heterogeneous in nature. Optimally assigning jobs to resources is one of the key issues in Grid environment. This work introduces a new Multi Dimensional Matchmaking Framework for resource allocation on computational grids. Making the environment intelligent and monitoring the dynamic behavior of resources gives accurate prediction of resource allocation. The dynamic dimensional based resource allocation enhances the overall performance of services.

Stock market prediction is the act of trying to determine the future value of a company stock of other financial Instrument traded on a financial exchange. This paper presents a comparison between, PSO and CSO trained Neural Network to predict the stock rates by preparing data which acts as input. The data is prepared in such a way that the external factors like traditional issues can be mitigated. Earlier Neural Network was trained using Back Propagation algorithm but it converges to local optima and cannot be applied to discrete functions. Sow we have chosen PSO and CSO optimization algorithm to train the Neural Network. The results show that training neural network with such data gives a better performance.

Handwritten text and character recognition is a challenging task compared to recognition of handwritten numeral and computer printed text due to its large variety in nature. Neural Network based approach provides most reliable performance in handwritten character and text recognition but recognition performance depends upon some important factors like no of training samples, reliable features and no of features per character, training time, variety of handwriting etc. Important features from different types of handwriting are collected and are fed to the neural network for training. More no of features increases testing efficiency but it take longer time to converge the error curve. To reduce this training time effectively proper algorithm should be chosen so that the system provides best train and test efficiency in least possible time that is to provide the system fastest intelligence. In this paper we have used

Scaled Conjugate Gradient Algorithm

, a second order training algorithm for training of neural network. It provides faster training with excellent test efficiency. A scanned handwritten text is taken as input and character level segmentation is done. Some important and reliable features from each character are extracted and used as input to a neural network for training. When the error level reaches into a satisfactory level (10

− 12

) weights are accepted for testing a test script. Finally a lexicon matching algorithm solves the minor misclassification problems.

Satellite-based Synthetic Aperture Radar (SAR) provides a powerful vessel surveillance capability in front of time consuming traditional reconnaissance methods. Nevertheless, due to the presence of speckle and to the reduced dimensions of the targets compared to the sensor resolution, the automatic interpretation of SAR images is often complicated even though vessels undetected are sometimes visible by eye. Therefore, the main difficulties of automatic ship detection will be reviewed and the main drawbacks will be identified here. As a result of this preliminary analysis, the application of transformations is found to be a useful mathematical object for this purpose. Hence based on the capabilities of the image analysis, the objective is achieved in such a way that it provides better understanding and further exploitation of satellite high resolution images. More specifically, a novel method for ship detection based on multiscale tools is proposed, in which SAR images are over here.

Creational Design Patterns play an important role in making the software applications most reusable and adaptable to future changes in software requirements. Creational patterns abstract (encapsulate) how objects are created in the software system. They enable client code independent of object creation, object composition and how the objects are represented. Abstract Factory creational design pattern is one of the object creational patterns which help software system independent of how objects are created. In this paper, we proposed and applied creational patterns (Abstract Factory and Singleton) to achieve the above mentioned objectives. Creation of Decorator Pattern objects in DMS application is accomplished by Abstract Factory and Singleton patterns. This change enabled the application more adaptable to the changes (e.g. any addition of new user) without changing the client code and enhanced the reusability of the system with increased ease of system maintenance. Proposed changes are applied to the DMS application using .NET framework, C# and ASP.NET.

The Scalable Geographic Based Multicast (SGBM) protocol is designed for the Mobile Adhoc Networks (MANET), which overcomes the difficulties in group membership management, multicast packet delivery and maintenance of multicast structure over the dynamic network. Furthermore a zone is divided based on the threshold value, so that the packet delivery is maintained easily and the time spent on the data forwarding will be less. The performance of the protocol was studied by extensive simulations. The result depicts that the protocol we designed is more reliable, efficient and scalable to a large group size or a network size.

A novel Median adjusted Constrained PDF based Histogram Equalization (MCPHE) technique for contrast enhancement is proposed in this paper. In this method, the probability density function of an image is modified by introducing constraints prior to the process of histogram equalization (HE). This technique of contrast enhancement takes control over the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment factor is then added to the result, which normalizes the change in the luminance level after enhancement. This factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of highly deviated intensities have greater impact in changing the contrast of an image. Experimental results show that the proposed method gives better results in terms of PSNR and SSIM values when compared to the existing histogram based equalization methods.

Recently with the wide development of computers, various forms of information exchange between human and computer are discovered. At present, interacting with the computer using speech is one of the active scientific research fields especially for people with disabilities who face variety of barriers to computer use. Such research in Automatic Speech Recognition (ASR) is investigated for different languages because each language has its specific features. This paper presents a speech recognition system for individually spoken word in Tamil language using multilayer feed forward network which falls under the category, "Networks for Classification and Prediction" and has widespread interesting applications and functions related to speech processing. To implement the above system, initially preprocessing is done with the input signal and the speech features being the main part of speech recognition system, are analyzed and extracted via Mel Frequency Cepstral Coefficients (MFCC). These feature vectors are given as the input to the Feed-Forward Neural Network for classifying and recognizing Tamil spoken word. Experiments are done with sample Tamil speech signals and its performance are measured based on Mean square error (MSE). The results indicate that the adopted network with specified parameters have produced the best MSE.

This paper presents an efficient compound image compression method based on block and layer–based segmentation techniques. Two hybrid models are proposed for segmenting compound images. The first model combines layer-based and block-based techniques for segmentation and compression of compound images. Several experiments were conducted to evaluate both models in terms of compression ratio, PSNR and time.

Mobile computing system is more popular as people need data on move. Many emerging mobile database applications demand broadcast based data dissemination, ie, the data broadcasted to more number of clients without any request. In this paper we address the problem of preserving data consistency and currency among mobile transactions, while the broadcast and update transactions executed concurrently on server without any control. We propose an efficient serializability based algorithm (IOBS) for ensuring the consistency of entire database. This algorithm has the following properties.

The order of update transactions seen by the different mobile clients will be same.

Consistency and currency are ensured without contacting the server.

The size of control information for conflict checking is smaller.

The multimedia information retrieval from distributed environment specifically from World Wide Web is considered as one of the challenging issues to be addressed by the researchers. It has been perceived that the description of images using low-level features apparently increases the semantic gap. The information present in a HTML document in the form of textual keywords can be extracted for capturing semantic information with the view to narrow the semantic gap. The high-level textual information of images can be extracted and associated with the textual keywords that narrow down the search space and improve the precision of retrieval. In this paper, a strength matrix, is being proposed, which is based on the frequency of occurrence of keywords and the textual information pertaining to image URLs. The strength of these textual keywords are estimated and used for associating these keywords with the images present in the documents. The high-level semantics of the image is described in the HTML documents in the form of image name, ALT tag, optional description, etc., is used for estimating the strength. The effectiveness of information retrieval of the proposed technique is found to be comparatively better than many of the recently proposed retrieval techniques. The experimental results of the proposed method endorse the fact that image retrieval using image information and textual keywords is better than those of the text based and the content-based approaches.

A new multiple access scheme using Multi-Symbol Encapsulated Orthogonal Frequency Division Multiplexing (MSE-OFDM) is proposed. This is basically a time division multiple access (TDMA) technique utilizing the MSE-OFDM. The Bit Error Rate (BER) performance of the TDMA system using conventional OFDM is slightly better than that of the proposed system. This weakness is compensated as the proposed technique exhibits an improvement in bandwidth efficiency compared to the TDMA using conventional OFDM.

Semantic web service discovery process consumes a significant amount of time even to perform a typical service match of limited functional capabilities. Modern business integration requires services need to be dynamically discovered and composed quickly. In this work, a similarity based clustering approach is suggested for quick elimination of irrelevant services during discovery. In this method, all the available published services are clustered by service description similarity in prior to semantic matching. This yields clusters of similar services. After services are clustered, when a query is submitted, firstly, a particular cluster to which the query belongs to is found out. Then semantic matching of capabilities will be performed only to that particular cluster ignoring all other service clusters as irrelevant to the query. The proposed method is tested for its performance with a small test collection and results are presented.

Automatic plant classification systems are essential for a wide range of applications including environment protection, plant resource survey, as well as for education. With the aid of advanced information technology, image processing and machine learning techniques, automatic plant identification and classification will enhance such systems with more functionality, such as automatic labeling and flexible searching. Image segmentation and object recognition are two aspects of digital image processing which are being increasingly used in many applications including leaf recognition. In this paper, the Preferential Image Segmentation (PIS) method is used to segment an object of interest from the original image. A probabilistic curve evolution method with particle filters is used to measure the similarity between shapes during matching process. The experimental results prove that the preferential image segmentation can be successfully applied in leaf recognition and segmentation from a plant image.

Geocasting is a special variant of multicasting, where data packet or message is transmitted to a predefined geographical location i.e., known as geocast region. The applications of geocasting in VANET are to disseminate information like, collision warning, advertising, alerts message, etc. The topology of VANET is changes frequently and rapidly due to the fast movement of vehicles. Therefore, the link time between two neighboring vehicles exist for a short span of time. This makes delivery of messages to a geocast region most challenging issue in VANET. In this paper, we have proposed an analytical model based on connectivity and selection of next node. For the better network connectivity in multi hop vehicular ad hoc network each cell should have at least one node. The numerical results show how connectivity between nodes depends on the transmission range of nodes.

Vehicle routing problem with time window (VRPTW) is a NP-Complete and a multi-objective problem. The problem involves optimizing a fleet of vehicles that are to serve a number of customers from a central depot. Each vehicle has limited capacity and each customer has a certain demand. Genetic Algorithms maintain a population of solutions by means of a crossover and mutation operators. For crossover and mutation, best cost route crossover techniques and exchange mutation procedure is used respectively. In this paper, we focus on three objectives of VRPTW i.e. number of vehicles, total cost (distance), and time window violation (routing time). The proposed Multi Objective Genetic Algorithm (MOGA) finds optimum solutions effectively.

The optimal Reactive Power Dispatch (RPD) problem is a nonlinear constrained optimization problem. This paper presents true multi-objective solution set for multi-objective RPD problem. The objective functions are real power loss minimization and control variable adjustment costs. In this paper, a Modified Non-Dominated Sorting Genetic Algorithm version II (MNSGA-II) is proposed for solving RPD problem. For maintaining good diversity in the performance of NSGA-II, the concept of Dynamic Crowding Distance (DCD) is implemented in NSGA-II algorithm and given name as MNSGA-II. The standard IEEE 30-bus and IEEE 118 bus test systems are used. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference Pareto front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures.The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPD problem.

In the modern world everything is faster. People are expecting optimality in each and every instance. In this today’s era, shopping is an inseparable & essential activity. But in reality it creates lot of stress and utilizes not only our time but also energy. Recently cities are equipped with shopping malls, but still confusion and unnecessary wandering are unavoidable. Already we proposed a novel idea called TESOR (Time and Energy Saving Optimal Routing) algorithm using shortest path algorithm as a base one. Now in this article we further extended the TESOR based on number of adult customer, which leads great time and energy saver one. With that Revised TESOR almost 50% reduction can be achieved in terms of distance travel and spending time. The simulation analysis shows the effectiveness of the algorithm with proper analysis and comparative chart. The target domain of this paper is that of providing 100% end user satisfaction.

In this paper, we present a background on Elliptic Curve Cryptography, along with the Montgomery operation, which plays major role on Elliptic Curve Cryptosystem and side channel attacks on EC-Montgomery operations. We have also provided a brief background on File Register architecture and proposed a new design level of EC-Montgomery addition algorithm. These design level architecture are designed in an efficient manner. Realizing the proposed design level of FPGA technology. Through this FPGA based technology very advantageous results were found when compared against other existing designs. Even though our design bears an extra computation time, it reduces the area space or register gate area space due to reassigning the design and power analysis attack.

This paper presents a newly devised noise filter viz., Modified Partial Differential Equation based Adaptive Two-Stage Median Filter (MPATS) to denoise the images corrupted by fixed-value impulse noise. The performance of the proposed filter is proved to be better in terms of Peak Signal - to - Noise Ratio and human visual perception. This filter is effective in denoising the highly corrupted images with the noise probability of even 90%.

This paper presents a computational intelligence technique for enhancing the performance of classifier using a proposed algorithm called Modified Genetic Search Algorithms (MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting and deleting bad candidate chromosomes, thereby reducing the number of individual chromosomes from search space and subsequent iterations in next generations. It addresses the strength of Modified Genetic Search algorithm combined with the Artificial Neural Network (ANN). In this work dynamic Backpropagation Neural Network is used. For training purpose, dynamic learning rate is used that causes the learning rate to decrease in subsequent epoch.

The combined MGSA-ANN is used for the classification of diabetes patients to identify positive and negative cases. It also discusses the main findings and concludes with promising result of the proposed model. The experimental results obtained by synergistic combination of Modified Genetic Search Algorithm with ANN surpass the performance of ANN by 1.4322%.

Access control mechanisms are an integral part of most modern software systems, but it is still not considered as an explicit part in the development process. Access control mechanisms and policies are generally added to existing systems as an afterthought, with all the problems of unsatisfied security requirements, integration difficulties, and mismatches between design models. In order to increase the overall system security, access control requirements should be taken into account early in the software development process. Due to the integration of Access control requirements early in the development process, potential security breaches can be detected and removed earlier. But, on the other hand, this integration results in spreading of access control functionalities across the software systems and making them tightly cohesive. When there is a need arises for updating the existing access control requirement, the changes would impact on several functional requirements. Moreover the access control requirements tend to evolve continually. Accordingly, the design phase should support both the static and dynamic changes of the access control requirements without making much impact on the functional requirements. To address this issue at the design level, we need an approach that could support to model the access control requirements along with the constraints and also capture the changes, without affecting the functional design. In this paper, we propose a Feature Conclave Model based on feature modeling, for analyzing and designing the evolving access control requirements along with the system design. The Feature Conclave Model views the access control requirements as “Features” and also provides step-by-step integration with the functional domain. Moreover, the new updates of the access control requirements can be represented as the variants and can easily be adapted within the system design.

Detection and Diagnosis of faults in pneumatic valve used in cooler water spray system in cement industry is of great practical significance and paramount importance for the safe operation of the plant. In this paper the dimensionality reduction techniques such as principal component analysis (PCA) are used to reduce the input features is proposed. PCA is used to extract the primary features associated with the pneumatic valve used in cooler water spray system. The training and testing data required for the dimensionality reduction technique such as PCA model were created at normal and faulty conditions of pneumatic valve in a real time laboratory experimental setup. The performance of the developed PCA model is compared with the MLFFNN (Multilayer Feed Forward Neural Network) trained by the back propagation algorithm. From the simulation results it is observed that the performance of PCA had the best classification properties when it is compared with the performance of ANN.

Sign language is the primary means of communication in the deaf community. The importance of sign language is emphasized by growing research activities and various funding agencies which deal with the technical aspects of deaf people’s communication needs. Signing is primarily done with the hands. In technical terms the hands are used to describe the so-called manual parameters, which are pose, posture, position and motion. General view based sign language recognition systems extract these parameters by a single camera view because it seems to be user friendly and hardware complexity; however it needs a high accuracy classifier for classification and recognition purpose. The decision making of the system in this work employs statistical classifiers namely Navie bayes and K-NN to recognize the sign language isolated signs. Indian sign language datasets is used for the training and performance evaluation of the system.It compares the ability to deploy the K-NN and the Naïve Bayesian classifiers in solving the sign classification problem. The impact of such study may reflect the exploration for using such algorithms in other similar applications such as text classification and the development of automated systems.

This paper proposes the agent based intelligent, adaptive learning or tutoring system (IATS). The proposed architecture of the agent based system provides the framework for the intelligent tutoring system. We define many agents like tutoring agent, student agent, student clustering agent, resource classification agent. The system model is developed using the O-MaSE development model. The clustering is done by the combination of Evolutionary clustering methods and fuzzy c-means clustering. The classification of the resources is representation of the resources in the tree based hierarchical structure from which the adaptive resources are generated. The system provides the adaptive course materials, adaptive user interface, and adaptive examinations to the learner.

The VLSI Physical design floorplanning is the process where circuit description is converted into geometric description. This NP-Optimization problem with non-slicing blocks is very hard to solve. Memetic algorithms are used to solve NP-Optimization problems. Memetic algorithms are a family of meta-heuristic search algorithms in which a rule-based representation of Local Search (LS) is co-adapted alongside the candidate solutions within a hybrid evolutionary system. However, they may execute for a long time for difficult problems, because of several fitness evaluations. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose to implement a parallel Memetic Algorithm (MA) on Graphical Processing Units. The General Purpose Graphical Processing Unit (GPGPU) is the complete parallel hardware which is best used for the Parallel Computing. The Parallel Memetic Algorithm we followed for an application perspective is a modified or hybrid genetic algorithm with extensive attributes to local search. The Parallel Memetic Algorithm operator gives a perfect exploration in the available search area and it is typical for addressing a modular assignment problem. It reduces the time efficiently for execution which is a boon for VLSI industry where we don’t have to spend more weeks for Physical design to be completed. We perform experiments to compare our parallel MA with an ordinary MA and demonstrate that the former is much more effective than the latter. Both fitness evaluation and genetic operations are spread over the GPU cores, allowing for the efficient processing of very large datasets. The parallel nature of Memetic algorithm can be best harnessed with help of GPGPU and significant changes in execution time and optimization are shown for VLSI Floorplanning problem.

Cloud Computing is accessing Services through Internet based on pay per usage model. Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) are available in Cloud. Cloud based products will eliminate the need to install and manage client rich applications. Cloud Service providers are helping companies to reduce the high cost infrastructure installation and maintenance cost. The customer is charged only for the resources consumed like utility based computing. Virtualization is a technique to implement cloud computing resources such as platform, application, storage, and network. The beauty of virtualization solutions is that you can run multiple virtual machines (VM) simultaneously on a single machine. This paper provides details about cloud computing issues, how virtualization techniques can be used to overcome those issues.

The appropriate usage of efficient testing technique at any stage of the Software Development Life Cycle (SDLC) is still in infant stage. There are a number of testing techniques at various phases of testing. Selection of the right testing technique at any stage is one of the critical problems. The selection method should not only take the subjective knowledge but also incorporate objective knowledge for the efficient selection of testing technique as per the requirements. Selection of testing technique at every stage of SDLC depends on many factors such as resources, schedule, cost of the project, etc. Thus we formulate the selecting of testing technique as a multi criteria decision making problem and propose an efficient solution.

In this paper we present a hybrid evolutionary algorithm (HEA) for solving the pagenumber problem. In HEA, random depth first search of the graph is used for placing the vertices on the spine and an edge embedding heuristic is designed to distribute the edges on a minimal number of pages. The results show that the algorithm achieves the optimal pagenumber for most of the standard graphs tested by us. HEA performance is also compared with the GA described by Kapoor et al. [1] on select instances of standard and random graphs. It is observed that HEA gives a better solution for most of the instances.

Web-based social networks (WBSNs) are a promising new paradigm for large scale distributed data management and collective intelligences. But the exponential growth of social networks poses a new challenge and presents opportunities for recommender systems, such as complicated nature of human to human interaction which comes into play while recommending people. Web based recommender systems (RSs) are the most notable application of the web personalization to deal with problem of information overload. In this paper, we present a Friend RS for WBSNs. Our contribution is three fold. First, we have identified appropriate attributes in a user profile and suggest suitable similarity computation formulae. Second, a real-valued Genetic algorithm is used to learn user preferences based on comparison of individual features to increase recommendation effectiveness. Finally, inorder to alleviate the sparsity problem of collaborative filtering, we have employed trust propagation techniques. Experimental results clearly demonstrate the effectiveness of our proposed schemes.

Cryptographic algorithms are useful for signing and verifying the authenticity of sender of the message. The verifier may not have the required computational ability and relies on a powerful server to aid the verification process. The server and the illegitimate prover/signer may collaborate and try to cheat the verifier. A legitimate prover can also repudiate the message sent by himself. In this paper we model the scenario where the legitimate, cheating or repudiating prover sign’s the message. The verifier then authenticates the message via an untrusted server. Specifications are written using CTL(Computational-Tree Logic). NuSMV(extension of Symbolic Model Verifier) is the tool used to verify the specifications.

In open distributed environment several users accesses the network resources on server, server will allow only authenticated users to access these resources. So it has become of prime importance for nodes communicating over a non-secure network to prove their identity to one another in a secure manner. Various authentication protocols is used for the this purpose, Kerberos protocol is one of them. Several versions of the protocol exist. The aim of this paper is modeling and verification of the Inter Realm Authentication and User to User Authentication in Kerberos protocol through NuSMV model checker. It also demonstrate that when presence of intruder in the system, and make use of service, it will generate the counter example.

The paper proposes a new Bluetooth security architecture based on user authentication and chaotic image encryption. The algorithm is able to overcome man in middle attack as well as privacy for image is ensured using a fast encryption algorithm based on chaotic encryption. Chaotic encryption produces the key using a pseudorandom number generator. It is a stream cipher method.

An increasing number of database queries are executed by interactive users and applications. Since the user is waiting for the database to respond with an answer, the initial response time of producing the first results is very important. The user can process the first results while the database system efficiently completes the entire query. The state-of-art join algorithms are not ideal for this setting. Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data is provided by autonomous data sources through heterogeneous network environments. The main advantage of adaptive join techniques is that they can start producing join results as soon as the first input tuples are available, thus improving pipelining by smoothing join result production and by masking source or network delays. Since the response time of the queries places a vital role in adaptive join, the join techniques like Hash Join, Sort Merge Join cannot be used because they require some prework before producing the join result. The only possible join technique that can be used in adaptive join is Nested Loop Join. In Nested Loop Join each row of the outer relation is compared with each row of the inner relation. The no. of comparisons done by the nested loop join can be reduced by using a technique called trace backing. In trace backing technique whenever a miss match occurs, the next tuple of the outer relation is compared with the mismatched inner relation tuple, instead of looping all the tuples of the inner relation.

The concept of wireless ATM is now being actively considered as a potential framework for next-generation wireless communication networks capable of supporting integrated multimedia services with different QoS requirements. Several key subsystem design issues for wired ATM and wireless networks needs to be readdressed in the scope of the wireless ATM, which has the capability to extend the statistical multiplexing of wired ATM network into the wireless medium. One of the key subsystem issues is the development of appropriate medium access control (MAC) protocol. The extension of the ATM network into wireless environment faces many interesting problems. The original ATM network was designed for high speed, noiseless, and reliable channels. None of these characteristics are applicable to the wireless channel. One of the critical aspects of a wireless ATM network is the Medium or Multi Access Control (MAC) Protocol used by the Mobile station (MS) to request service from the BS, which has to consider the Quality of Service (QoS) of the specific applications. This paper analyzes recently proposed MAC protocols, particularly those of Demand Assignment Multiple Access protocols using TDMA technique with Frequency Division Duplex (FDD). It also gives performance measures of two best suited protocols for wireless network environment Distributed Queuing Request Update Multiple Access (DQRUMA) protocol and Adaptive Request Channel Multiple Access (ARCMA) protocol.

This paper presents an optimum speaker independent isolated digit recognizer for Malayalam language. The system employs Perceptual Linear Predictive (PLP) cepstral coefficient for speech parameterization. The powerful and well accepted pattern recognition technique Hidden Markov Model is used for acoustic modeling. The training data base has the utterance of 21 speakers from the age group ranging from 20 to 40 years and the sound is recorded in the normal office environment where each speaker uttered digits zero to nine independently. The system obtained an accuracy of 99.5% with the unseen data.

Agent Based Modeling (ABM) is usually referred to as the third way of doing modeling & simulation in contrast to the historically popular equation-based macrosimulations and microsimulations. In ABM a system is modeled as a set of autonomous agents who interact with each other and also with the environment. The agents represent various actors in the system and the environment represents agent’s surroundings and the overall context. ABM has been successfully applied to model and analyze a number of complex social processes and structures, ranging from habitation patterns to spread of culture. In this paper, we present our experimental work on applying ABM to model individual voting preferences. The model explores process of formation of voting preferences, the factors governing it and its effect on the final voting pattern. We have incorporated social influence theory in our model and experimented with various settings. The paper concludes with a short discussion of the results and their relevance.

In this paper, we present an evaluation of diagnosis of dementia using texture analysis of brain MRI with wavelets and further classification by BackPropagation Network. The tests were conducted on 3D brain MRI data extracted from OASIS database. The classification is based on the following steps: First, the region of interest is extracted from the MRI images by wavelets, Gray level occurance matrix (GLCM) and Haralick features. Gabor features were characterized by the distribution of histogram of wavelet coefficients. These features were segregated into three datasets. The first dataset containing the GLCM features, the second data set has the Haralick features and the third dataset has Gabor wavelet based Haralick features. Classification was done by backpropagation network based on 3 feature vectors. From the analysis it has been found that the average efficiency of Gabor combined with Haralick features is around 97% for all types of datasets, and the average efficiency value for GLCM is 86 % and that of Haralick features was 90%. From the comparison of the average efficiency of the wavelet families, statistical features extracted from Gabor wavlets provides better efficiency than the other two methods.

Rootkits are a set of software tools used by an attacker to gain unauthorized access into a system, thereby providing him with privilege to access sensitive data, conceal its own existence and allowing him to install other malicious software. They are difficult to detect due to their elusive nature. Modern rootkit attacks mainly focus on modifying operating system kernel. Existing techniques for detection rely mainly on saving the system state before detection and comparing it with the infected state. Efficient detection is possible by properly differentiating malicious and non malicious activities taking place in a kernel. In this paper we present a novel anomaly detection method for kernel level rootkits based on k-means clustering algorithm.

Data mining is the process of extracting interesting patterns from data. Data mining is recently proving very effective in business decision making and is becoming a widely used strategy to improve CRM (Customer Relationship Management). CRM is the process of managing a good relationship with customer and improving the profitability of their interactions with the customer. Data mining is widely used particularly in handling large data sets as in telecom sector. Clustering is a popular mining strategy that separates those data into subsets called clusters. This research work focuses on comparing the two main approaches of clustering soft clustering and hard clustering namely the Kmeans and Fuzzy C Means (FCM) clustering algorithms on large telecom data to determine the churn ratio as a measure to enhance CRM. It is observed that FCM outperforms Kmeans in estimating churn ratio accurately and is more effective in supporting CRM.

Traditional banking was based on paper documents like ‘deposit form’, ‘withdrawal form’ etc. The comfort level for the customer on paper based business was high as they can retain the paper and show as proof in case of disputes. The present Internet and Core banking systems which are widely in use are transaction based. One drawback of the present situation is that customer is not able to keep a copy of the transaction due to various reasons like multiple channels and lack of suitable infrastructure at client end etc. As a via media, we tried to bridge the gap and see whether we can give the comfort and trust levels of the paper document coupled with the intelligence of the technology.

In this model we propose a method to describe financial product like “Fixed Deposits” using XML technology. At present, banks give the deposits in paper form with the deposit amount, maturity date and interest rate prescribed in the document. The banks system calculates the interest and pays the customer. The paper itself can’t calculate interest. It just helps the customer as a proof alone. In our model, we are trying to see whether we can embed the interest calculation algorithm into XML based deposit and give to the customer, so that customer himself can find how much he gets using tools like XSI. This will act as a good verification mechanism, instead of solely depending on banks’ systems. To show case this model as a POC, we used XSD, XML and XSI (an in house tool with IDRBT).

Gender classification algorithms uses a number of dimensionality reduction techniques. These techniques are used to remove linear and non–linear dependency among the data. Most popular techniques are Principal Component Analysis (PCA) and Independent Component analysis (ICA). PCA makes the data uncorrelated while ICA gives independent data. In this approach, infomax ICA has been used for gender recognition and it is outperforming PCA. To perform classification using ICA features, Support Vector Machine (SVM) has been used. For larger training set SVM is performing with an accuracy of 98%. The accuracy values are obtained for change in size of testing set and the proposed system performs with an average accuracy of 96%.

The

Simple Motif Problem

(SMP) is: given a set of strings

Y

 = {

y

0

,

y

1

,…,

y

n

 − 1

} built from a finite alphabet Σ,

p

 > 0 an integer and

q

 ≤ 

n

a quorum, find all the simple motifs of length at most

p

that occurs in at least

q

strings of

Y

.

This paper presents an experimental evaluation of algorithms dealing with SMP and using a minimal forbidden pattern approach. The experiments are concerned both with running times and space consumption.

The work proposed here, suggests a protocol that provides anonymity to negotiating parties and protects negotiation information without the use of encryption. The protocol is developed to ensure maximum security while causing minimum delay. Decentralization of routing and service information protects the information. The protocol makes use of K-anonymity principle to ensure security.

In Educational data mining, learning process evaluation reveals the students’ involvement in learning. It leads to improve the quality and success rate of the students in educational environment. Students’ intellectual performance varies in learning environment and evaluated by different criteria. This study helps to find out the intelligent quotient level of the student and their performance, which are dissimilar in a classroom. Stanford Binet Intelligence Scale and Criterion Reference Model are used to evaluate the intelligence and performance of the students. Machine learning techniques are employed to find the intelligent quotient and performance of rural and urban students using students’ dataset. Model based clustering technique is used to determine the similarity in the students’ dataset based on the nature of the intelligent quotient and performance. Each cluster reveals the identity based on students intelligence level. The performance also categorized on descriptive modeling. Multilayer Perceptron technique classifies the intelligent level of the students and their performance. The study determines the association between intelligent level of the rural and urban students and performance through descriptive and predictive modeling. This analysis recommends the teaching community to provide right training to the rural students for their improvement of academic competence.

One of the most common resources for assistance to users while dealing with programming languages is online help, which is topic-oriented, procedural or reference information delivered through online software. This research paper reports observations about organizational factors with online helps for programming languages. These observations are viz. Organized Index of Content, Hyperlinks, Grouping of Items, Use of Tables / Charts / figures, Scrolling Effect, Cascading Effect, Multilingual Support and Help in Video Format. They are compiled based on authors’ experience, discussions with active users and literature study about online helps. The users with programming languages can be classified as tool users, programmers, developers and architects. We find that organized index, hyperlinks, grouping of items and use of tables / charts / figures are important for all users with programming languages. These organizational factors directly affect the usability of online helps with programming languages. They should be considered by designers / developers of such online helps so that their users are encouraged in efficient usage and referencing while dealing with products / systems like programming languages.

Peer-to-Peer (P2P) as well as mobile ad hoc networks (MANETs) follows the same idea of creating a network without the help of central entities. However, P2P and MANETs operate on different network layers. A combination of both, creates new services and possibilities, but also faces several problems. As smarter mobile devices are increasingly adopted by the wider public, to access p2p applications, the question of who to trust while on the move becomes a crucial one. When operating within ad-hoc type p2p networks, a trust-based framework is the logical solution to provide ways to distinguish the most trustworthy neighbors. This promotes reliable information transfer, and encourages content and resource sharing. This paper proposes a scheme to set up such trusted paths, based upon aggregating individual peers’ trust, given out by their nearest neighbor.

Text mining provides methods to retrieve and extract information contained in free-text automatically. The advances in molecular biology techniques have given rise to high throughput biology that produces tremendous data and related publications in the past decades. Here the efficient constituents of Co-reference resolution incorporating Natural Language Processing (NLP) to interpret Gene expression are focused. It may overcome the challenges and limitations of text mining in biological data for resolving unsolved problems and this paper describes a new phase of text mining process to uncover interesting term correlations, genomic term identification in curation process , identification of biological relations and help the biologists in their analysis of complex problems. The new phase of text mining process is organized in four tasks of namely use

NLP, find correlated terms, Co-reference resolution and built a structured database

.

At present, real time navigation systems rely on kalman filtering for integrating global positioning system (GPS) and inertial navigation system (INS) in order to provide feasible solution for reliable navigation. But there exist some inadequacies related to the stochastic error models of inertial sensors, requirement of prior knowledge in fusing data and long design time. Moreover, recently artificial intelligence (AI) techniques based on static neural networks were suggested in place of kalman filter. But there also exist drawbacks like inadequate training during GPS absence, less reliability and more training time. To solve this problem, a new dynamic architecture based on Input-delayed model has been developed. This paper, therefore proposes a scheme based on Improved Input-delayed dynamic neural network (IIDDNN) to model the samples of INS values with respect to GPS values as target vectors (Normal mode). This model is capable of providing a reliable and accurate solution especially for GPS absence (prediction mode). The prediction mode output obtained using IIDDNN is compared with the performance of normal mode of IIDDNN. The proposed IIDDNN model is evaluated using test data of fixed trajectory path for an aircraft vehicle.

Artificial neural network has been shown to be an efficient tool for non-parametric modeling of data in a variety of different contexts where the output is the nonlinear function of inputs. Neural networks are the preferred tool for many predictive data mining applications because of their power, flexibility and ease of use. In the present study Feed-Forward Network based Artificial Neural Network (ANN) model is used as a method to predict the groundwater levels. The ANN model was trained using back propagated algorithm with two hidden layer, and with logsig activation function. The models are evaluated using three statistical performance criteria namely Mean Average Error (MAE), Root Mean Squared Error (RMSE) and Regression coefficient (R²). The results of the study clearly showed that ANN can be used to predict groundwater level in a hard rock region with reasonably good accuracy even in case of limited data situation.

Routing in wireless network is a key research area which establishes path between source and destination node pairs. In this paper, we have designed and evaluated an energy-efficient and fault tolerant Greedy perimeter Stateless routing (EFGPSR) protocol for wireless Ad hoc network. The proposed protocol is divided into four phases: Fault testing phase, Planarization phase, Energy efficient greedy forwarding phase and Energy efficient perimeter forwarding phase. In fault testing phase, all nodes come to know about their fault free neighbours. Next is planarization phase which is subset of perimeter forwarding phase and can be done reactively or proactively. Next are energy efficient greedy forwarding and perimeter forwarding phases. Both these phases try to maintain balance between the metrics to choose the next hop (i.e. distance from destination in greedy forwarding phase and minimum angle w.r.t the line connecting the forwarding node and destination in perimeter forwarding phase) and selection of node having highest energy among the neighbouring node to extend network lifetime. Evaluation and comparison of GPSR and EFGPSR is done through NS-2 simulator. Simulation shows that EFGPSR performs better in terms of increasing the network lifetime, successful packet delivery ratio with insignificant increase in number of hop count.

Mobile Devices have been gaining increasing acceptance for developing Multimedia-rich applications. Everyday new Technologies in Mobile Devices have been developed by developers and are competing with the Previous in Market. Nowadays developing applications for Smart phones is common today and nearly thousands of new applications are coming to the Market every day. Software firms have also developed many programming platforms and tools for the developers to write programs for Mobile Devices. But so far there is no Specific IDE developed to create mobile application easily by just Drag and Drop method to make even the non-programmers to develop application for the smart phones.

This paper presents an IDE for developing Mobile Application without Programming for Smart phones. The IDE allows non-programmer to develop a mobile application for smart phones efficiently. The IDE has navigation control tools in iconic format that can be used to incorporate within the application by drag and drop method and directly installed to the smart phones as applications. With the help of the GUI Builders and ADT plug-in the IDE is created and tested against the Android Mobile Phones.

For a large training data set, the memory space required to store the kernel matrix is a difficult task for the solution of QP problems for SVR. Support Vector Regression (SVR) is used to approximate the function. So we are proposing the slope based partition algorithm, which automatically evolves the partitions based on change in slope. In this paper we are improving the performance of function approximation with SVM by preprocessing the dataset with Fuzzy c-Mean clustering (FCM) and slope based partition methods. Using FCM and slope, we are portioning the data, and for every partitioned dataset we are finding the function approximation, and aggregating the result. The results indicate that Root Mean Square Error (RMSE) has been reduced with the partitioned data, compare to the processing entire dataset, with the increase in performance approximately 40%

The design details of a PIC-controller based Doubly Fed Induction Generator (DFIG) for wind power generation is described. The proposed PIC-DFIG not only provides a low cost Solution to today’s electric power needs, but is also aimed at delivering better power quality, making it ideally suited for wind energy conversion systems operating at variable wind speeds and connected to variable and often, dynamic utility loads. Power quality improvement is achieved by cancellation of the largest and the most annoying harmonics in the supply grid. Additionally, power factor correction and adaptive response to variable reactive loading are also addressed by this design. The proposed design involves two current-controlled power converters connected through a slip ring assembly. The rotor control is achieved using a commercially available PIC controller (model 16F877A) employing a position sensorless, field oriented control algorithm. Some preliminary simulation efforts and modeling scenarios are also presented.

A new packet scheduling scheme for Mobile WiMAX network is proposed. The network architecture includes one BS and several MSs which are in different locations. The uplink traffic from Mobile Stations (MS’s) that belong to various classes to the Base Station (BS) is scheduled.It is assumed that the MS’s request the BS for bandwidth by a Bandwidth request and the BS station grants the bandwidth. The available slots for scheduling of uplink traffic is done based on the predefined priorities of the traffic classes. The BS then allocates the bandwidth to each MS based on the class it belongs and distance from the BS. All the mobile WiMAX parameters like transmit power, receive gain, transmit gain, mobility have been considered for simulation. Results indicate a clear reduction in delay and increase in throughput for different classes of flows compared to the existing Quality of Service based 2-Tier Ad-Hoc Scheduling Scheme (2T-AHSS).