A Survey on Authentication and Key Agreement Protocols in Heterogeneous Networks

Abstract: Unlike current closed systems such as 2nd and 3rd generations where the core network is controlled by a sole network operator, multiple network operators will coexist and manage the core network in Next Generation Networks (NGNs). This open architecture and the collaboration between different network operators will support ubiquitous connectivity and thus enhances users' experience. However, this brings to the fore certain security issues which must be addressed, the most important of which is the initial Authentication and Key Agreement (AKA) to identify and authorize mobile nodes on these various networks. This paper looks at how existing research efforts the HOKEY WG, Mobile Ethernet and 3GPP frameworks respond to this new environment and provide security mechanisms. The analysis shows that most of the research had realized the openness of the core network and tried to deal with it using different methods. These methods will be extensively analysed in order to highlight their strengths and weaknesses.


Algorithmic Simplicity and Relevance

Abstract: The human mind is known to be sensitive to complexity. For instance, the visual system reconstructs hidden parts of objects following a principle of maximum simplicity. We suggest here that higher cognitive processes, such as the selection of relevant situations, are sensitive to variations of complexity. Situations are relevant to human beings when they appear simpler to describe than to generate. This definition offers a predictive (i.e. falsifiable) model for the selection of situations worth reporting (interestingness) and for what individuals consider an appropriate move in conversation.


Heuristic based task scheduling in multiprocessor systems with genetic algorithm by choosing the eligible processor

Abstract: In multiprocessor systems, one of the main factors of systems' performance is task scheduling. The well the task be distributed among the processors the well be the performance. Again finding the optimal solution of scheduling the tasks into the processors is NP-complete, that is, it will take a lot of time to find the optimal solution. Many evolutionary algorithms (e.g. Genetic Algorithm, Simulated annealing) are used to reach the near optimal solution in linear time. In this paper we propose a heuristic for genetic algorithm based task scheduling in multiprocessor systems by choosing the eligible processor on educated guess. From comparison it is found that this new heuristic based GA takes less computation time to reach the suboptimal solution.


Role of Ranking Algorithms for Information Retrieval

Abstract: As the use of web is increasing more day by day, the web users get easily lost in the web's rich hyper structure. The main aim of the owner of the website is to give the relevant information according their needs to the users. We explained the Web mining is used to categorize users and pages by analyzing user's behavior, the content of pages and then describe Web Structure mining. This paper includes different Page Ranking algorithms and compares those algorithms used for Information Retrieval. Different Page Rank based algorithms like Page Rank (PR), WPR (Weighted Page Rank), HITS (Hyperlink Induced Topic Selection), Distance Rank and EigenRumor algorithms are discussed and compared. Simulation Interface has been designed for PageRank algorithm and Weighted PageRank algorithm but PageRank is the only ranking algorithm on which Google search engine works.


CrowdER: Crowdsourcing Entity Resolution

Abstract: Entity resolution is central to data integration and data cleaning. Algorithmic approaches have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a more accurate but expensive (and slow) way to bring human insight into the process. Previous work has proposed batching verification tasks for presentation to human workers but even with batching, a human-only approach is infeasible for data sets of even moderate size, due to the large numbers of matches to be tested. Instead, we propose a hybrid human-machine approach in which machines are used to do an initial, coarse pass over all the data, and people are used to verify only the most likely matching pairs. We show that for such a hybrid system, generating the minimum number of verification tasks of a given size is NP-Hard, but we develop a novel two-tiered heuristic approach for creating batched tasks. We describe this method, and present the results of extensive experiments on real data sets using a popular crowdsourcing platform. The experiments show that our hybrid approach achieves both good efficiency and high accuracy compared to machine-only or human-only alternatives.


Uncertain Time-Series Similarity: Return to the Basics

Abstract: In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others. Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms. In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach, and further compare these alternatives with two additional techniques that were carefully studied before. We conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results, which suggest that a fruitful research direction is to take into account the temporal correlations in the time series. Based on our evaluations, we also provide guidelines useful for the practitioners in the field.


Statistical Distortion: Consequences of Data Cleaning

Abstract: We introduce the notion of statistical distortion as an essential metric for measuring the effectiveness of data cleaning strategies. We use this metric to propose a widely applicable yet scalable experimental framework for evaluating data cleaning strategies along three dimensions: glitch improvement, statistical distortion and cost-related criteria. Existing metrics focus on glitch improvement and cost, but not on the statistical impact of data cleaning strategies. We illustrate our framework on real world data, with a comprehensive suite of experiments and analyses.


Towards Energy-Efficient Database Cluster Design

Abstract: Energy is a growing component of the operational cost for many "big data" deployments, and hence has become increasingly important for practitioners of large-scale data analysis who require scale-out clusters or parallel DBMS appliances. Although a number of recent studies have investigated the energy efficiency of DBMSs, none of these studies have looked at the architectural design space of energy-efficient parallel DBMS clusters. There are many challenges to increasing the energy efficiency of a DBMS cluster, including dealing with the inherent scaling inefficiency of parallel data processing, and choosing the appropriate energy-efficient hardware. In this paper, we experimentally examine and analyze a number of key parameters related to these challenges for designing energy-efficient database clusters. We explore the cluster design space using empirical results and propose a model that considers the key bottlenecks to energy efficiency in a parallel DBMS. This paper represents a key first step in designing energy-efficient database clusters, which is increasingly important given the trend toward parallel database appliances.


Experiments with Game Tree Search in Real-Time Strategy Games

Abstract: Game tree search algorithms such as minimax have been used with enormous success in turn-based adversarial games such as Chess or Checkers. However, such algorithms cannot be directly applied to real-time strategy (RTS) games because a number of reasons. For example, minimax assumes a turn-taking game mechanics, not present in RTS games. In this paper we present RTMM, a real-time variant of the standard minimax algorithm, and discuss its applicability in the context of RTS games. We discuss its strengths and weaknesses, and evaluate it in two real-time games.


Scheduling Data Intensive Workloads through Virtualization on MapReduce based Clouds

Abstract: MapReduce has become a popular programming model for running data intensive applications on the cloud. Completion time goals or deadlines of MapReduce jobs set by users are becoming crucial in existing cloud-based data processing environments like Hadoop. There is a conflict between the scheduling MR jobs to meet deadlines and "data locality" (assigning tasks to nodes that contain their input data). To meet the deadline a task may be scheduled on a node without local input data for that task causing expensive data transfer from a remote node. In this paper, a novel scheduler is proposed to address the above problem which is primarily based on the dynamic resource reconfiguration approach. It has two components: 1) Resource Predictor: which dynamically determines the required number of Map/Reduce slots for every job to meet completion time guarantee; 2) Resource Reconfigurator: that adjusts the CPU resources while not violating completion time goals of the users by dynamically increasing or decreasing individual VMs to maximize data locality and also to maximize the use of resources within the system among the active jobs. The proposed scheduler has been evaluated against Fair Scheduler on virtual cluster built on a physical cluster of 20 machines. The results demonstrate a gain of about 12% increase in throughput of Jobs


Comparison of different T-norm operators in classification problems

Abstract: Fuzzy rule based classification systems are one of the most popular fuzzy modeling systems used in pattern classification problems. This paper investigates the effect of applying nine different T-norms in fuzzy rule based classification systems. In the recent researches, fuzzy versions of confidence and support merits from the field of data mining have been widely used for both rules selecting and weighting in the construction of fuzzy rule based classification systems. For calculating these merits the product has been usually used as a T-norm. In this paper different T-norms have been used for calculating the confidence and support measures. Therefore, the calculations in rule selection and rule weighting steps (in the process of constructing the fuzzy rule based classification systems) are modified by employing these T-norms. Consequently, these changes in calculation results in altering the overall accuracy of rule based classification systems. Experimental results obtained on some well-known data sets show that the best performance is produced by employing the Aczel-Alsina operator in terms of the classification accuracy, the second best operator is Dubois-Prade and the third best operator is Dombi. In experiments, we have used 12 data sets with numerical attributes from the University of California, Irvine machine learning repository (UCI).


MIDI-LAB, a Powerful Visual Basic Program for Creating MIDI Music

Abstract: Creating MIDI music can be a practical challenge. In the past, working with it was difficult and frustrating to all but the most accomplished and determined. Now, however, we are offering a powerful Visual Basic program called MIDI-LAB, that is easy to learn, and instantly rewarding to even the newest users. MIDI-LAB has been developed to give users the ability to quickly create music with a limitless variety of tunes, tempos, speeds, volumes, instruments, rhythms and major scales. This program has a simple, intuitive, and user-friendly interface, which provides a straightforward way to enter musical data with Numbered Musical Notation (NMN) and immediately create MIDI music. The key feature of this program is the digitalization of music input. It vastly simplifies creating, editing, and saving MIDI music. MIDI-LAB can be used virtually anywhere to write music for entertainment, teaching, computer games, and mobile phone ringtones.


AODVSEC: A Novel Approach to Secure Ad Hoc on-Demand Distance Vector (AODV) Routing Protocol from Insider Attacks in MANETs

Abstract: Mobile Ad hoc Network (MANET) is a collection of mobile nodes that can communicate with each other using multihop wireless links without requiring any fixed based-station infrastructure and centralized management. Each node in the network acts as both a host and a router. In such scenario, designing of an efficient, reliable and secure routing protocol has been a major challenging issue over the last many years. Numerous schemes have been proposed for secure routing protocols and most of the research work has so far focused on providing security for routing using cryptography. In this paper, we propose a novel approach to secure Ad hoc On-demand Distance Vector (AODV) routing protocol from the insider attacks launched through active forging of its Route Reply (RREP) control message. AODV routing protocol does not have any security provision that makes it less reliable in publicly open ad hoc network. To deal with the concerned security attacks, we have proposed AODV Security Extension (AODVSEC) which enhances the scope of AODV for the security provision. We have compared AODVSEC with AODV and Secure AODV (SAODV) in normal situation as well as in presence of the three concerned attacks viz. Resource Consumption (RC) attack, Route Disturb (RD) attack, Route Invasion (RI) attack and Blackhole (BH) attack. To evaluate the performances, we have considered Packet Delivery Fraction (PDF), Average End-to-End Delay (AED), Average Throughput (AT), Normalized Routing Load (NRL) and Average Jitter and Accumulated Average Processing Time.


An Internet Approach for Engineering Student Exercises

Abstract: An approach for engineering student exercises using the Internet is described. In this approach, for a given exercise, each student receives the same problem, but with different data. The exercise content can be static or dynamic, and the dynamic form can be timeless or real-time. The implementation provides immediate feedback to the students, letting them know if their submitted answers are correct. Student results for each exercise are recorded in log files which are available to the instructor. Example exercises from engineering computer security and cryptography courses are presented.


Block-Relaxation Methods for 3D Constant-Coefficient Stencils on GPUs and Multicore CPUs

Abstract: Block iterative methods are extremely important as smoothers for multigrid methods, as preconditioners for Krylov methods, and as solvers for diagonally dominant linear systems. Developing robust and efficient algorithms suitable for current and evolving GPU and multicore CPU systems is a significant challenge. We address this issue in the case of constant-coefficient stencils arising in the solution of elliptic partial differential equations on structured 3D uniform and adaptively refined grids. Robust, highly parallel implementations of block Jacobi and chaotic block Gauss-Seidel algorithms with exact inversion of the blocks are developed using different parallelization techniques. Experimental results for NVIDIA Fermi GPUs and AMD multicore systems are presented.


Determination Of Optimal Number Of Clusters In Wireless Sensor Networks

Abstract: Prolonged network lifetime, scalability and efficient load balancing are essential for optimal performance of a wireless sensor network. Clustering provides an effective way of extending the lifetime of a sensor network. Clustering is the process that divides sensor networks into smaller localized group (called clusters) of members with a cluster head. Clustering protocols need to elect optimal number of clusters in hierarchically structured wireless sensor networks. Any clustering scheme that elects clusters uniformly (irrespective of the distance from Base Station) incurs excessive energy usage on clusters proximal and distant to Base Station. In single hop networks a gradual increment in the energy depletion rate is observed as the distance from the cluster head increases. This work focuses on the analysis of wasteful energy consumption within a uniform cluster head election model (EPEM) and provides an analytic solution to reduce the overall consumption of energy usage amongst the clusters elected in a wireless sensor network. A circular model of sensor network is considered, where the sensor nodes are deployed around a centrally located Base Station. The sensor network is divided into several concentric rings centred at the Base Station. A model, Unequal Probability Election Model (UEPEM), which elects cluster heads non-uniformly is proposed. The probability of cluster head election depends on the distance from the Base Station. UEPEM reduces the overall energy usage by about 21% over EPEM. The performance of UEPEM improves as the number of rings is increased.


An algorithm for improving the quality of compacted JPEG image by minimizes the blocking artifacts

Abstract: The Block Transform Coded, JPEG- a lossy image compression format has been used to keep storage and bandwidth requirements of digital image at practical levels. However, JPEG compression schemes may exhibit unwanted image artifacts to appear - such as the 'blocky' artifact found in smooth/monotone areas of an image, caused by the coarse quantization of DCT coefficients. A number of image filtering approaches have been analyzed in literature incorporating value-averaging filters in order to smooth out the discontinuities that appear across DCT block boundaries. Although some of these approaches are able to decrease the severity of these unwanted artifacts to some extent, other approaches have certain limitations that cause excessive blurring to high-contrast edges in the image. The image deblocking algorithm presented in this paper aims to filter the blocked boundaries. This is accomplished by employing smoothening, detection of blocked edges and then filtering the difference between the pixels containing the blocked edge. The deblocking algorithm presented has been successful in reducing blocky artifacts in an image and therefore increases the subjective as well as objective quality of the reconstructed image.


Goldbach Ellipse Sequences for Cryptographic Applications

Abstract: The paper studies cryptographically useful properties of the sequence of the sizes of Goldbach ellipses. We show that binary subsequences based on this sequence have useful properties. They can be used to generate keys and to provide an index-based mapping to numbers. The paper also presents a protocol for secure session keys that is based on Goldbach partitions.


Tag Spotting at the Interference Range

Abstract: In wireless networks, the presence of interference among wireless links in- troduces dependencies among flows that do not share a single link or node. As a result, when designing a resource allocation scheme, be it a medium access scheduler or a flow rate controller, one needs to consider the interdependence among nodes within interference range of each other. Specifically, control plane information needs to reach nearby nodes which often lie outside the communi- cation range, but within the interference range of a node of interest. But how can one communicate control plane information well beyond the existing communication range? To address this fundamental need we introduce tag spotting. Tag spotting refers to a communication system which allows re- liable control data transmission at SNR values as low as 0 dB. It does this by employing a number of signal encoding techniques including adding redundancy to multitone modulation, shaping the spectrum to reduce inter-carrier interfer- ence, and the use of algebraic coding. Making use of a detection theory-based model we analyze the performance achievable by our modulation as well as the trade-off between the rate of the information transmitted and the likelihood of error. Using real-world experiments on an OFDM system built with software radios, we show that we can transmit data at the target SNR value of 0 dB with a 6% overhead; that is, 6% of our packet is used for our low-SNR decodable tags (which carry up to a couple of bytes of data in our testbed), while the remain- ing 94% is used for traditional header and payload data. We also demonstrate via simulations how tag spotting can be used in implementing fair and efficient rate control and scheduling schemes.


Inferring SQL Queries Using Program Synthesis

Abstract: Developing high-performance applications that interact with databases is a difficult task, as developers need to understand both the details of the language in which their applications are written in, and also the intricacies of the relational model. One popular solution to this problem is the use of object-relational mapping (ORM) libraries that provide transparent access to the database using the same language that the application is written in. Unfortunately, using such frameworks can easily lead to applications with poor performance because developers often end up implementing relational operations in application code, and doing so usually does not take advantage of the optimized implementations of relational operations, efficient query plans, or push down of predicates that database systems provide. In this paper we present QBS, an algorithm that automatically identifies fragments of application logic that can be pushed into SQL queries. The QBS algorithm works by automatically synthesizing invariants and postconditions for the original code fragment. The postconditions and invariants are expressed using a theory of ordered relations that allows us to reason precisely about the contents and order of the records produced even by complex code fragments that compute joins and aggregates. The theory is close in expressiveness to SQL, so the synthesized postconditions can be readily translated to SQL queries. Using 40 code fragments extracted from over 120k lines of open-source code written using the Java Hibernate ORM, we demonstrate that our approach can convert a variety of imperative constructs into relational specifications.


Comparison between Performances of Channel Estimation Techniques for CP-LTE and ZP-LTE Downlink Systems

Abstract: In this paper, we propose to evaluate the performance of channel estimation techniques for Long Term Evolution (LTE) Downlink systems based on Zero Padding technique (ZP) instead of Cyclic Prefixing (CP). LTE Downlink system is a multiuser system based on a MIMO-OFDMA technology. Usually, in OFDM systems, a guard interval is inserted in order to mitigate both inter-carrier interference (ICI) and inter-symbol interference (ISI). LTE Downlink systems are based on CP-OFDM technique which consists of a copy a last OFDM symbols inserted at the beginning of each transmitted OFDM symbol. Although this technique shows good performances, the CP-LTE system suffers from a power efficiency loss.With the number of present OFDM symbols in LTE Downlink radio frame, the bandwidth loss becomes more important. Instead of CP, we propose to evaluate the performance of ZP-LTE systems in order to avoid the power efficiency .In this paper, we interest to evaluate the performance of channel estimation techniques for the two LTE Downlink systems. Simulations results show that although ZP-LTE systems outperform CP-LTE Downlink systems in terms of power efficiency, the CP-LTE systems show better performance than ZP-LTE systems and especially for high SNR values. MATLAB Monte-Carlo simulations are used to evaluate the performance of LS, LMMSE and Lr-LMMSE estimators in terms of Mean Square Error (MSE) and Bit Error Rate (BER) for 2x2 LTE Downlink systems.


Discovery of Malicious Attacks to Improve Mobile Collaborative Learning (MCL)

Abstract: Mobile collaborative learning (MCL) is highly acknowledged and focusing paradigm in eductional institutions and several organizations across the world. It exhibits intellectual synergy of various combined minds to handle the problem and stimulate the social activity of mutual understanding. To improve and foster the baseline of MCL, several supporting architectures, frameworks including number of the mobile applications have been introduced. Limited research was reported that particularly focuses to enhance the security of those pardigms and provide secure MCL to users. The paper handles the issue of rogue DHCP server that affects and disrupts the network resources during the MCL. The rogue DHCP is unauthorized server that releases the incorrect IP address to users and sniffs the traffic illegally. The contribution specially provides the privacy to users and enhances the security aspects of mobile supported collaborative framework (MSCF). The paper introduces multi-frame signature-cum anomaly-based intrusion detection systems (MSAIDS) supported with novel algorithms through addition of new rules in IDS and mathematcal model. The major target of contribution is to detect the malicious attacks and blocks the illegal activities of rogue DHCP server. This innovative security mechanism reinforces the confidence of users, protects network from illicit intervention and restore the privacy of users. Finally, the paper validates the idea through simulation and compares the findings with other existing techniques.


An Improved Watchdog Technique Based On Power-Aware Hierarchical Design For Ids In Wireless Sensor Networks

Abstract: Preserving security and confidentiality in wireless sensor networks (WSN) are crucial. Wireless sensor networks in comparison with wired networks are more substantially vulnerable to attacks and intrusions. In WSN, a third person can eavesdrop to the information or link to the network. So, preventing these intrusions by detecting them has become one of the most demanding challenges. This paper, proposes an improved watchdog technique as an effective technique for detecting malicious nodes based on a power aware hierarchical model. This technique overcomes the common problems in the original Watchdog mechanism. The main purpose to present this model is reducing the power consumption as a key factor for increasing the network's lifetime. For this reason, we simulated our model with Tiny-OS simulator and then, compared our results with non hierarchical model to ensure the improvement. The results indicate that, our proposed model is better in performance than the original models and it has increased the lifetime of the wireless sensor nodes by around 2611.492 seconds for a network with 100 sensors.


A study on non-destructive method for detecting Toxin in pepper using Neural networks

Abstract: Mycotoxin contamination in certain agricultural systems have been a serious concern for human and animal health. Mycotoxins are toxic substances produced mostly as secondary metabolites by fungi that grow on seeds and feed in the field, or in storage. The food-borne Mycotoxins likely to be of greatest significance for human health in tropical developing countries are Aflatoxins and Fumonisins. Chili pepper is also prone to Aflatoxin contamination during harvesting, production and storage periods.Various methods used for detection of Mycotoxins give accurate results, but they are slow, expensive and destructive. Destructive method is testing a material that degrades the sample under investigation. Whereas, non-destructive testing will, after testing, allow the part to be used for its intended purpose. Ultrasonic methods, Multispectral image processing methods, Terahertz methods, X-ray and Thermography have been very popular in nondestructive testing and characterization of materials and health monitoring. Image processing methods are used to improve the visual quality of the pictures and to extract useful information from them. In this proposed work, the chili pepper samples will be collected, and the X-ray, multispectral images of the samples will be processed using image processing methods. The term "Computational Intelligence" referred as simulation of human intelligence on computers. It is also called as "Artificial Intelligence" (AI) approach. The techniques used in AI approach are Neural network, Fuzzy logic and evolutionary computation. Finally, the computational intelligence method will be used in addition to image processing to provide best, high performance and accurate results for detecting the Mycotoxin level in the samples collected.


A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

Abstract: This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.


Inverse Reinforcement Learning with Gaussian Process

Abstract: We present new algorithms for inverse reinforcement learning (IRL, or inverse optimal control) in convex optimization settings. We argue that finite-space IRL can be posed as a convex quadratic program under a Bayesian inference framework with the objective of maximum a posterior estimation. To deal with problems in large or even infinite state space, we propose a Gaussian process model and use preference graphs to represent observations of decision trajectories. Our method is distinguished from other approaches to IRL in that it makes no assumptions about the form of the reward function and yet it retains the promise of computationally manageable implementations for potential real-world applications. In comparison with an establish algorithm on small-scale numerical problems, our method demonstrated better accuracy in apprenticeship learning and a more robust dependence on the number of observations.


A DDS-Based Scalable and Reconfigurable Framework for Cyber-Physical Systems

Abstract: Cyber-Physical Systems (CPSs) involve the interconnection of heterogeneous computing devices which are closely integrated with the physical processes under control. Often, these systems are resource-constrained and require specific features such as the ability to adapt in a timeliness and efficient fashion to dynamic environments. Also, they must support fault tolerance and avoid single points of failure. This paper describes a scalable framework for CPSs based on the OMG DDS standard. The proposed solution allows reconfiguring this kind of systems at run-time and managing efficiently their resources.


On distributed monitoring of asynchronous systems

Abstract: Distributed systems are notoriously difficult to understand and analyze in order to assert their correction w.r.t. given properties. They often exhibit a huge number of different behaviors, as soon as the active entities (peers, agents, processes, etc) behave in an asynchronous manner. Already the modelization of such systems is a non-trivial task, let alone their formal verification. The purpose of this paper is to discuss the problem of distributed monitoring on a simple model of finite-state distributed automata based on shared actions, called asynchronous automata. Monitoring is a question related to runtime verification: assume that we have to check a property $L$ against an unknown or very complex system $A$, so that classical static analysis is not possible. Therefore instead of model-checking a monitor is used, that checks the property on the underlying system at runtime. We are interested here in monitoring distributed systems modeled as asynchronous automata. It is natural to require that monitors should be of the same kind as the underlying system, so we consider here distributed monitoring. A distributed monitor does not have a global view of the system, therefore we propose the notion of locally monitorable trace language. Our main result shows that if the distributed alphabet of actions is connected and if $L$ is a set of infinite traces such that both $L$ and its complement $L^c$ are countable unions of locally safety languages, then $L$ is locally monitorable. We also show that over infinite traces, recognizable countable unions of locally safety languages are precisely the complements of deterministic languages.


Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis

Abstract: Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.


Applying CEGAR to the Petri Net State Equation

Abstract: We propose a reachability verification technique that combines the Petri net state equation (a linear algebraic overapproximation of the set of reachable states) with the concept of counterexample guided abstraction refinement. In essence, we replace the search through the set of reachable states by a search through the space of solutions of the state equation. We demonstrate the excellent performance of the technique on several real-world examples. The technique is particularly useful in those cases where the reachability query yields a negative result: While state space based techniques need to fully expand the state space in this case, our technique often terminates promptly. In addition, we can derive some diagnostic information in case of unreachability while state space methods can only provide witness paths in the case of reachability.


Securing Speech in GSM Networks using DES with Random Permutation and Inversion Algorithm

Abstract: Global System for Mobile Communications (GSM) is one of the most commonly used cellular technologies in the world. One of the objectives in mobile communication systems is the security of the exchanged data. GSM employs many cryptographic algorithms for security like A5/1, A5/2 and A5/3. Even so, these algorithms do not provide sufficient level of security for protecting the confidentiality of GSM. Therefore, it is desirable to increase security by additional encryption methods. This paper presents a voice encryption method called: "DES with Random permutation and Inversion", based on current voice channel, which overcomes data channel's insufficiencies and solves the problem of penetrating the RPE-LTP vocoder by the encrypted voice. The proposed method fulfils an end-to-end secured communication in the GSM; insure a good compatibility to all GSM networks, and easy implementation without any modification in these systems.


Elimination of ISI Using Improved LMS Based Decision Feedback Equalizer

Abstract: This paper deals with the implementation of Least Mean Square (LMS) algorithm in Decision Feedback Equalizer (DFE) for removal of Inter Symbol Interference (ISI) at the receiver. The channel disrupts the transmitted signal by spreading it in time. Although, the LMS algorithm is robust and reliable, it is slow in convergence. In order to increase the speed of convergence, modifications have been made in the algorithm where the weights get updated depending on the severity of disturbance.


Curved Space Optimization: A Random Search based on General Relativity Theory

Abstract: Designing a fast and efficient optimization method with local optima avoidance capability on a variety of optimization problems is still an open problem for many researchers. In this work, the concept of a new global optimization method with an open implementation area is introduced as a Curved Space Optimization (CSO) method, which is a simple probabilistic optimization method enhanced by concepts of general relativity theory. To address global optimization challenges such as performance and convergence, this new method is designed based on transformation of a random search space into a new search space based on concepts of space-time curvature in general relativity theory. In order to evaluate the performance of our proposed method, an implementation of CSO is deployed and its results are compared on benchmark functions with state-of-the art optimization methods. The results show that the performance of CSO is promising on unimodal and multimodal benchmark functions with different search space dimension sizes.


Space-Efficient Circuit Evaluation

Abstract: We prove that uniform circuits of size n can be evaluated in space O(n/log n). Thus, Space(O(n)) is not in uniform Size(o(n*log n)). For uniformity, we only require that the circuit is O(n/log n)-Space uniform. We also generalize the construction to prove that a machine with O(n^delta) (delta<1) internal storage and O(2^n^delta) length single-bit-access read-write RAM that does only O(n) RAM reads (1 bit per read) can be simulated in space O(n * log log n / log n).


Point-Separable Classes of Simple Computable Planar Curves

Abstract: In mathematics curves are typically defined as the images of continuous real functions (parametrizations) defined on a closed interval. They can also be defined as connected one-dimensional compact subsets of points. For simple curves of finite lengths, parametrizations can be further required to be injective or even length-normalized. All of these four approaches to curves are classically equivalent. In this paper we investigate four different versions of computable curves based on these four approaches. It turns out that they are all different, and hence, we get four different classes of computable curves. More interestingly, these four classes are even point-separable in the sense that the sets of points covered by computable curves of different versions are also different. However, if we consider only computable curves of computable lengths, then all four versions of computable curves become equivalent. This shows that the definition of computable curves is robust, at least for those of computable lengths. In addition, we show that the class of computable curves of computable lengths is point-separable from the other four classes of computable curves.


Analysis of Statistical Hypothesis based Learning Mechanism for Faster Crawling

Abstract: The growth of world-wide-web (WWW) spreads its wings from an intangible quantities of web-pages to a gigantic hub of web information which gradually increases the complexity of crawling process in a search engine. A search engine handles a lot of queries from various parts of this world, and the answers of it solely depend on the knowledge that it gathers by means of crawling. The information sharing becomes a most common habit of the society, and it is done by means of publishing structured, semi-structured and unstructured resources on the web. This social practice leads to an exponential growth of web-resource, and hence it became essential to crawl for continuous updating of web-knowledge and modification of several existing resources in any situation. In this paper one statistical hypothesis based learning mechanism is incorporated for learning the behavior of crawling speed in different environment of network, and for intelligently control of the speed of crawler. The scaling technique is used to compare the performance proposed method with the standard crawler. The high speed performance is observed after scaling, and the retrieval of relevant web-resource in such a high speed is analyzed.


Minimal TestCase Generation for Object-Oriented Software with State Charts

Abstract: Today statecharts are a de facto standard in industry for modeling system behavior. Test data generation is one of the key issues in software testing. This paper proposes an reduction approach to test data generation for the state-based software testing. In this paper, first state transition graph is derived from state chart diagram. Then, all the required information are extracted from the state chart diagram. Then, test cases are generated. Lastly, a set of test cases are minimized by calculating the node coverage for each test case. It is also determined that which test cases are covered by other test cases. The advantage of our test generation technique is that it optimizes test coverage by minimizing time and cost. The present test data generation scheme generates test cases which satisfy transition path coverage criteria, path coverage criteria and action coverage criteria. A case study on Railway Ticket Vending Machine (RTVM) has been presented to illustrate our approach.


L1 Splines for Robust, Simple, and Fast Smoothing of Grid Data

Abstract: Splines are a popular and attractive way of smoothing noisy data. Computing splines involves minimizing a functional which is a linear combination of a fitting term and a regularization term. The former is classically computed using a (weighted) L2 norm while the latter ensures smoothness. Thus, when dealing with grid data, the optimization can be solved very efficiently using the DCT. In this work we propose to replace the L2 norm in the fitting term with an L1 norm, leading to automatic robustness to outliers. To solve the resulting minimization problem we propose an extremely simple and efficient numerical scheme based on split-Bregman iteration combined with DCT. Experimental validation shows the high-quality results obtained in short processing times.


Learning pseudo-Boolean k-DNF and Submodular Functions

Abstract: We prove that any submodular function f: {0,1}^n -> {0,1,...,k} can be represented as a pseudo-Boolean 2k-DNF formula. Pseudo-Boolean DNFs are a natural generalization of DNF representation for functions with integer range. Each term in such a formula has an associated integral constant. We show that an analog of Hastad's switching lemma holds for pseudo-Boolean k-DNFs if all constants associated with the terms of the formula are bounded. This allows us to generalize Mansour's PAC-learning algorithm for k-DNFs to pseudo-Boolean k-DNFs, and hence gives a PAC-learning algorithm with membership queries under the uniform distribution for submodular functions of the form f:{0,1}^n -> {0,1,...,k}. Our algorithm runs in time polynomial in n, k^{O(k \log k / \epsilon)}, 1/\epsilon and log(1/\delta) and works even in the agnostic setting. The line of previous work on learning submodular functions [Balcan, Harvey (STOC '11), Gupta, Hardt, Roth, Ullman (STOC '11), Cheraghchi, Klivans, Kothari, Lee (SODA '12)] implies only n^{O(k)} query complexity for learning submodular functions in this setting, for fixed epsilon and delta. Our learning algorithm implies a property tester for submodularity of functions f:{0,1}^n -> {0, ..., k} with query complexity polynomial in n for k=O((\log n/ \loglog n)^{1/2}) and constant proximity parameter \epsilon.


LOTKIP: Low Overhead TKIP Optimization for Ad Hoc Wireless Network

Abstract: Temporal Key Integrity Protocol (TKIP) is a provisional solution for Wired Equivalent Privacy (WEP) security loopholes present in already widely deployed legacy 802.11 wireless devices. In this work, we model and analyse the computational complexity of TKIP security mechanism and propose an optimised implementation, called LOTKIP, to decrease processing overhead for better energy efficient security performance. The LOTKIP improvements are based on minimising key mixing redundancy and a novel frame encapsulation with low overhead. We simulate and compare LOTKIP with baseline TKIP in terms of complexity and energy consumption for ad hoc wireless network security. From simulation results, we demonstrate that LOTKIP executes with lower computational complexity, hence, with faster encryption time and more energy-efficient.


Analytical Study of Pre-congestion notification (PCN) techniques

Abstract: Maintaining the quality of service (QOS) and controlling the network congestion are quite complicated tasks. They cause degrading the performance of the network, and disturbing the continuous communication process. To overcome these issues, one step towards this dilemma has been taken in form of Pre-congestion notification (PCN) technique. PCN uses a packet marking technique within a PCN domain over IP networks. It is notified by egress node that works as guard at entry point of network. Egress node gives feedback to communicating servers whether rate on the link is exceeded than configured admissible threshold or within the limit. Based on this feedback, admission decisions are taken to determine whether to allow/block new coming flows or terminate already accepted. The actual question is about selection of right algorithm for PCN domain. In this paper, we investigate the analytical behavior of some known PCN algorithms. We make slide modifications in originality of PCN algorithms without disquieting working process in order to employ those within similar types of scenarios. Our goal is to simulate them either in highly congested or less congested realistic scenarios. On the basis of simulation done in ns2, we are able to recommend each PCN algorithm for specific conditions. Finally, we develop a benchmark that helps researchers and scientific communities to pick the right algorithm. Furthermore, the benchmark is designed to achieve specific objectives according to the users' requirements without congesting the network.


A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem

Abstract: Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesman problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a measure of problem difficulty for 2-opt we use the approximation ratio that it achieves on a given instance. Our investigations point out important features that make TSP instances hard or easy to be approximated by 2-opt.


Minimizing Electricity Theft using Smart Meters in AMI

Abstract: Global energy crises are increasing every moment. Every one has the attention towards more and more energy production and also trying to save it. Electricity can be produced through many ways which is then synchronized on a main grid for usage. The main issue for which we have written this survey paper is losses in electrical system. Weather these losses are technical or non-technical. Technical losses can be calculated easily, as we discussed in section of mathematical modeling that how to calculate technical losses. Where as nontechnical losses can be evaluated if technical losses are known. Theft in electricity produce non-technical losses. To reduce or control theft one can save his economic resources. Smart meter can be the best option to minimize electricity theft, because of its high security, best efficiency, and excellent resistance towards many of theft ideas in electromechanical meters. So in this paper we have mostly concentrated on theft issues.


A New Direction for Counting Perfect Matchings

Abstract: In this paper, we present a new exact algorithm for counting perfect matchings, which relies on neither inclusion-exclusion principle nor tree-decompositions. For any bipartite graph of $2n$ nodes and $\Delta n$ edges such that $\Delta \geq 3$, our algorithm runs with $O^{\ast}(2^{(1 - 1/O(\Delta \log \Delta))n})$ time and exponential space. Compared to the previous algorithms, it achieves a better time bound in the sense that the performance degradation to the increase of $\Delta$ is quite slower. The main idea of our algorithm is a new reduction to the problem of computing the cut-weight distribution of the input graph. The primary ingredient of this reduction is MacWilliams Identity derived from elementary coding theory. The whole of our algorithm is designed by combining that reduction with a non-trivial fast algorithm computing the cut-weight distribution. To the best of our knowledge, the approach posed in this paper is new and may be of independent interest.


Monitoring and Controlling Power using Zigbee Communications

Abstract: Smart grid is a modified form of electrical grid where generation, transmission, distribution and customers are not only connected electrically but also through strong communication network with each other as well as with market, operation and service provider. For achieving good communication link among them, it is very necessary to find suitable protocol. In this paper, we discuss different hardware techniques for power monitoring, power management and remote power controlling at home and transmission side and also discuss the suitability of Zigbee for required communication link. Zigbee has major role in monitoring and direct load controlling for efficient power utilization. It covers enough area needed for communication and it works on low data rate of 20Kbps to 250Kbps with minimum power consumption. This paper describes the user friendly control home appliances, power on/off through the internet, PDA using Graphical User Interface (GUI) and through GSM cellular mobile phone.


Modeling Propagation Characteristics for Arm-Motion in Wireless Body Area Sensor Networks

Abstract: To monitor health information using wireless sensors on body is a promising new application. Human body acts as a transmission channel in wearable wireless devices, so electromagnetic propagation modeling is well thought-out for transmission channel in Wireless Body Area Sensor Network (WBASN). In this paper we have presented the wave propagation in WBASN which is modeled as point source (Antenna), close to the arm of the human body. Four possible cases are presented, where transmitter and receiver are inside or outside of the body. Dyadic Green's function is specifically used to propose a channel model for arm motion of human body model. This function is expanded in terms of vector wave function and scattering superposition principle. This paper describes the analytical derivation of the spherical electric field distribution model and the simulation of those derivations.


Energy Efficient Wireless Communication using Genetic Algorithm Guided Faster Light Weight Digital Signature Algorithm (GADSA)

Abstract: In this paper GA based light weight faster version of Digital Signature Algorithm (GADSA) in wireless communication has been proposed. Various genetic operators like crossover and mutation are used to optimizing amount of modular multiplication. Roulette Wheel selection mechanism helps to select best chromosome which in turn helps in faster computation and minimizes the time requirements for DSA. Minimization of number of modular multiplication itself a NP-hard problem that means there is no polynomial time deterministic algorithm for this purpose. This paper deals with this problem using GA based optimization algorithm for minimization of the modular multiplication. Proposed GADSA initiates with an initial population comprises of set of valid and complete set of individuals. Some operators are used to generate feasible valid offspring from the existing one. Among several exponents the best solution reached by GADSA is compared with some of the existing techniques. Extensive simulations shows competitive results for the proposed GADSA.


Multilayer Perceptron Guided Key Generation Through Mutation with Recursive Replacement in Wireless Communication (MLPKG)

Abstract: In this paper, a multilayer perceptron guided key generation for encryption/decryption (MLPKG) has been proposed through recursive replacement using mutated character code generation for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights or hidden units of this selected hidden layer help to form a secret session key. The plain text is encrypted using mutated character code table. Intermediate cipher text is yet again encrypted through recursive replacement technique to from next intermediate encrypted text which is again encrypted to form the final cipher text through chaining, cascaded xoring of multilayer perceptron generated session key. If size of the final block of intermediate cipher text is less than the size of the key then this block is kept unaltered. Receiver will use identical multilayer perceptron generated session key for performing deciphering process for getting the recursive replacement encrypted cipher text and then mutated character code table is used for decoding. Parametric tests have been done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed technique.


HSEP: Heterogeneity-aware Hierarchical Stable Election Protocol for WSNs

Abstract: Wireless Sensor Networks (WSNs) are increasing to handle complex situations and functions. In these networks some of the nodes become Cluster Heads (CHs) which are responsible to aggregate data of from cluster members and transmit it to Base Stations (BS). Those clustering techniques which are designed for homogenous network are not enough efficient for consuming energy. Stable Election Protocol (SEP) introduces heterogeneity in WSNs, consisting of two type of nodes. SEP is based on weighted election probabilities of each node to become CH according to remaining energy of nodes. We propose Heterogeneity-aware Hierarchal Stable Election Protocol (HSEP) having two level of energies. Simulation results show that HSEP prolongs stability period and network lifetime, as compared to conventional routing protocols and having higher average throughput than selected clustering protocols in WSNs.


A Large Population Size Can Be Unhelpful in Evolutionary Algorithms

Abstract: The utilization of populations is one of the most important features of evolutionary algorithms (EAs). There have been many studies analyzing the impact of different population sizes on the performance of EAs. However, most of such studies are based computational experiments, except for a few cases. The common wisdom so far appears to be that a large population would increase the population diversity and thus help an EA. Indeed, increasing the population size has been a commonly used strategy in tuning an EA when it did not perform as well as expected for a given problem. He and Yao (2002) showed theoretically that for some problem instance classes, a population can help to reduce the runtime of an EA from exponential to polynomial time. This paper analyzes the role of population further in EAs and shows rigorously that large populations may not always be useful. Conditions, under which large populations can be harmful, are discussed in this paper. Although the theoretical analysis was carried out on one multi-modal problem using a specific type of EAs, it has much wider implications. The analysis has revealed certain problem characteristics, which can be either the problem considered here or other problems, that lead to the disadvantages of large population sizes. The analytical approach developed in this paper can also be applied to analyzing EAs on other problems.


A Comprehensive Survey of MAC Protocols for Wireless Body Area Networks

Abstract: In this paper, we present a comprehensive study of Medium Access Control (MAC) protocols developed for Wireless Body Area Networks (WBANs). In WBANs, small batteryoperated on-body or implanted biomedical sensor nodes are used to monitor physiological signs such as temperature, blood pressure, ElectroCardioGram (ECG), ElectroEncephaloGraphy (EEG) etc. We discuss design requirements for WBANs with major sources of energy dissipation. Then, we further investigate the existing designed protocols for WBANs with focus on their strengths and weaknesses. Paper ends up with concluding remarks and open research issues for future work.