A Strategy to enable Prefix of Multicast VoD through dynamic buffer allocation

Abstract: In this paper we have proposed a dynamic buffer allocation algorithm for the prefix, based on the popularity of the videos. More cache blocks are allocated for most popular videos and a few cache blocks are allocated for less popular videos. Buffer utilization is also maximized irrespective of the load on the Video-on-Demand system. Overload can lead the server getting slowed down. By storing the first few seconds of popular video clips, a multimedia local server can shield the users from the delay, throughput, and loss properties of the path between the local server and the central server. The key idea of controlled multicast is used to allow clients to share a segment of a video stream even when the requests arrive at different times. This dynamic buffer allocation algorithm is simulated and its performance is evaluated based on the buffer utilization by multimedia servers and average buffer allocation for the most popular videos. Our simulation results shows efficient utilization of network bandwidth and reduced hard disk utilization hence resulting in increase in the number of requests being served.


Geometric Programming Problem with Co-Efficients and Exponents Associated with Binary Numbers

Abstract: Geometric programming (GP) provides a power tool for solving a variety of optimization problems. In the real world, many applications of geometric programming (GP) are engineering design problems in which some of the problem parameters are estimating of actual values. This paper develops a solution procedure to solve nonlinear programming problems using GP technique by splitting the cost coefficients, constraint coefficients and exponents with the help of binary numbers. The equivalent mathematical programming problems are formulated to find their corresponding value of the objective function based on the duality theorem. The ability of calculating the cost coefficients, constraint coefficients and exponents developed in this paper might help lead to more realistic modeling efforts in engineering design areas. Standard nonlinear programming software has been used to solve the proposed optimization problem. Two numerical examples are presented to illustrate the method.


Shape-Adaptive Motion Estimation Algorithm for MPEG-4 Video Coding

Abstract: This paper presents a gradient based motion estimation algorithm based on shape-motion prediction, which takes advantage of the correlation between neighboring Binary Alpha Blocks (BABs), to match with the Mpeg-4 shape coding case and speed up the estimation process. The PSNR and computation time achieved by the proposed algorithm seem to be better than those obtained by most popular motion estimation techniques.


Adjacency Matrix based method to compute the node connectivity of a Computer Communication Network

Abstract: Paper has been withdrawn due to non-compliance with IJCSI terms and conditions.


M-Banking Security - a futuristic improved security approach

Abstract: In last few decades large technology development raised various new needs. Financial sector has also no exception. People are approaching all over the world to fulfill there dreams. Any sector needs to understand changing need of customer. In order to satisfy financial need for customer banks are taking help of new technology such as internet. Only problem remain is of security. The aim of this work is to provide a secure environment in terms of security for transaction by various ways. In order to improve security we are making use of "Steganography" technique in the way never used before. Task of enhancing security include construction of formula for both data encryption and also for hiding pattern. Server should not process any fake request hence concept of custom "Session id" and "Request id" is introduced. Implementation of such a security constraints in banking sector not only help to serve customer in better way but also make customer confident and satisfy.


Phase-Only Planar Antenna Array Synthesis with Fuzzy Genetic Algorithms

Abstract: This paper describes a new method for the synthesis of planar antenna arrays using fuzzy genetic algorithms (FGAs) by optimizing phase excitation coefficients to best meet a desired radiation pattern. We present the application of a rigorous optimization technique based on fuzzy genetic algorithms (FGAs), the optimizing algorithm is obtained by adjusting control parameters of a standard version of genetic algorithm (SGAs) using a fuzzy controller (FLC) depending on the best individual fitness and the population diversity measurements (PDM). The presented optimization algorithms were previously checked on specific mathematical test function and show their superior capabilities with respect to the standard version (SGAs). A planar array with rectangular cells using a probe feed is considered. Included example using FGA demonstrates the good agreement between the desired and calculated radiation patterns than those obtained by a SGA.


Adaptation of TURN protocol to SIP protocol

Abstract: Today, SIP is a protocol par Excellence in the field of communication over Internet. But, the fact that it belongs to the application layer constitutes a weakness vis-a-vis the NAT traversal. This weakness is due to the way in which the server replies to the requests of clients on the one hand. On the other, it is caused by the dynamic allocation of UDP ports for emission and reception of packets RTP/RTCP. The TURN Protocol may face this weakness. However, its use requires a certain number of exchanges between the clients and a TURN server before establishing the multimedia sessions and this increase the latent time. In this article, we propose to adapt TURN protocol for applications based on SIP protocol such as telephony over Internet, conference video, etc. This adaptation optimises the establishment of multimedia sessions by integrating a manager of TCP connections and multimedia flow controller into SIP Proxy server.


RFID Applications: An Introductory and Exploratory Study

Abstract: RFID is not a new technology and has passed through many decades of use in military, airline, library, security, healthcare, sports, animal farms and other areas. Industries use RFID for various applications such as personal/vehicle access control, departmental store security, equipment tracking, baggage, fast food establishments, logistics, etc. The enhancement in RFID technology has brought advantages that are related to resource optimization, increased efficiency within business processes, and enhanced customer care, overall improvements in business operations and healthcare. Our research is part of a big project; its aim is to produce a model for mobile technology implementation of hospital patients' movement process. However, the focus of this paper is to explore the main RFID components, i.e. the tag, antenna and reader. The results of the investigations conducted on the three RFID components will be used to develop our research model.


The Design of Circuit-Measuring Collaborative Learning System with Embedded Broker

Abstract: Recently, the academic community has been giving much attention to Cooperative Learning System, a group learning method combined with pedagogy and social psychology. It allows group members to gain knowledge through collaborations and interactions. Nowadays, most Internet cooperative learning systems are designed to provide students mainly with a convenient online environment to study theoretical courses but rarely with an online environment to operate practical instruments. Hence, this paper designed a 3D online cooperative learning system for operating virtual instruments with circuit-measuring function. By integrating with Virtual Reality, Remote Control Parameter Transmission and embedded system techniques, this system gives learners not only a cooperative learning environment via networking to jointly operate the 3D virtual instruments (for example, multi-meters, power supplies and oscilloscopes) but also the functions of instant messages and 3D puzzles to interact with one another. Therefore, learners can effectively improve learning interests and results.


Implementation of an Innovative Bio Inspired GA and PSO Algorithm for Controller design considering Steam GT Dynamics

Abstract: The Application of Bio Inspired Algorithms to complicated Power System Stability Problems has recently attracted the researchers in the field of Artificial Intelligence. Low frequency oscillations after a disturbance in a Power system, if not sufficiently damped, can drive the system unstable. This paper provides a systematic procedure to damp the low frequency oscillations based on Bio Inspired Genetic (GA) and Particle Swarm Optimization (PSO) algorithms. The proposed controller design is based on formulating a System Damping ratio enhancement based Optimization criterion to compute the optimal controller parameters for better stability. The Novel and contrasting feature of this work is the mathematical modeling and simulation of the Synchronous generator model including the Steam Governor Turbine (GT) dynamics. To show the robustness of the proposed controller, Non linear Time domain simulations have been carried out under various system operating conditions. Also, a detailed Comparative study has been done to show the superiority of the Bio inspired algorithm based controllers over the Conventional Lead lag controller.


Significant Interval and Frequent Pattern Discovery in Web Log Data

Abstract: There is a considerable body of work on sequence mining of Web Log Data. We are using One Pass frequent Episode discovery (or FED) algorithm, takes a different approach than the traditional apriori class of pattern detection algorithms. In this approach significant intervals for each Website are computed first (independently) and these interval used for detecting frequent patterns/Episode and then the Analysis is performed on Significant Intervals and frequent patterns That can be used to forecast the user's behavior using previous trends and this can be also used for advertising purpose. This type of applications predicts the Website interest. In this approach, time-series data are folded over a periodicity (day, week, etc.) Which are used to form the Interval? Significant intervals are discovered from these time points that satisfy the criteria of minimum confidence and maximum interval length specified by the user.


Efficient Packet Forwarding Approach in Vehicular Ad Hoc Networks Using EBGR Algorithm

Abstract: VANETs (Vehicular Ad hoc Networks) are highly mobile wireless ad hoc networks and will play an important role in public safety communications and commercial applications. Routing of data in VANETs is a challenging task due to rapidly changing topology and high speed mobility of vehicles. Conventional routing protocols in MANETs (Mobile Ad hoc Networks) are unable to fully address the unique characteristics in vehicular networks. In this paper, we propose EBGR (Edge Node Based Greedy Routing), a reliable greedy position based routing approach to forward packets to the node present in the edge of the transmission range of source/forwarding node as most suitable next hop, with consideration of nodes moving in the direction of the destination. We propose Revival Mobility model (RMM) to evaluate the performance of our routing technique. This paper presents a detailed description of our approach and simulation results show that packet delivery ratio is improved considerably compared to other routing techniques of VANET.


Framework for Visualizing Model-Driven Software Evolution and its Application

Abstract: Software Visualization encompasses the development and evaluation of methods for graphically representing different aspects of methods of software, including its structure, execution and evolution. Creating visualizations helps the user to better understand complex phenomena. It is also found by the software engineering community that visualization is essential and important. In order to visualize the evolution of the models in Model-Driven Software Evolution, authors have proposed a framework which consists of 7 key areas (views) and 22 key features for the assessment of Model Driven Software Evolution process and addresses a number of stakeholder concerns. The framework is derived by the application of the Goal Question Metric Paradigm. This paper aims to describe an application of the framework by considering different visualization tools/CASE tools which are used to visualize the models in different views and to capture the information of models during their evolution. Comparison of such tools is also possible by using the framework.


ICT in Universities of the Western Himalayan Region of India II: A Comparative SWOT Analysis

Abstract: This study presents a comparative SWOT analysis to comprehend the pattern of development of ICT within six universities of western Himalayan region of India. With the objective of achieving quality and excellence in higher education system in the region, this study provides a basis to decision makers to exploit opportunities and minimize the external threats. The SWOT analysis of different universities, placed under three categories, has been undertaken within the four-tier framework used earlier by the authors. Guided by the initiatives of National Mission on Education through ICT (NMEICT) for SWOT analysis, findings of this paper reveal, relative consistency of these three categories of universities, with the earlier study. A few suggestions, as opportunities, with an emphasis on problem solving orientation in higher education, have been made to strengthen the leadership of universities in the field of ICT.


Stochastic Model Based Proxy Servers Architecture for VoD to Achieve Reduced Client Waiting Time

Abstract: In a video on demand system, the main video repository may be far away from the user and generally has limited streaming capacities. Since a high quality video's size is huge, it requires high bandwidth for streaming over the internet. In order to achieve a higher video hit ratio, reduced client waiting time, distributed server's architecture can be used, in which multiple local servers are placed close to clients and, based on their regional demands video contents are cached dynamically from the main server. As the cost of proxy server is decreasing and demand for reduced waiting time is increasing day by day, newer architectures are explored, innovative schemes are arrived at. In this paper we present novel 3 layer architecture, includes main multimedia server, a Tracker and Proxy servers. This architecture targets to optimize the client waiting time. We also propose an efficient prefix caching and load sharing algorithm at the proxy server to allocate the cache according to regional popularity of the video. The simulation results demonstrate that it achieves significantly lower client's waiting time, when compared to the other existing algorithms.


Modified EESM Based Link Adaptation Algorithm for Multimedia Transmission in Multicarrier Systems

Abstract: The previous link adaptation algorithms on ofdm based systems use equal modulation order for all sub carrier index within a block. For multimedia transmission using ofdm as the modulation technique, unequal constellation is used within one ofdm subcarrier block, a set of subcarriers for audio and another set for video transmissions. A generic model has been shown for such a transmission and link adaptation algorithm has been proposed using EESM (Effective Exponential SNR mapping) method as basic method. Mathematical model has been derived for the channel based on bivariate Gaussian distribution in which the amplitude varies two dimensionally in the same envelope. From the Moment generating function of bivariate distribution, Probability of error has been theoretically derived. Results have been shown for BER performance of an ofdm system using unequal constellation. BER performances have been shown for different values of correlation parameter and fading figure.


Reliable Mining of Automatically Generated Test Cases from Software Requirements Specification (SRS)

Abstract: Writing requirements is a two-way process. In this paper we use to classify Functional Requirements (FR) and Non Functional Requirements (NFR) statements from Software Requirements Specification (SRS) documents. This is systematically transformed into state charts considering all relevant information. The current paper outlines how test cases can be automatically generated from these state charts. The application of the states yields the different test cases as solutions to a planning problem. The test cases can be used for automated or manual software testing on system level. And also the paper presents a method for reduction of test suite by using mining methods thereby facilitating the mining and knowledge extraction from test cases.


Detecting Bots Based on Keylogging Activities

Abstract: A bot is a piece of software that is usually installed on an infected machine without the user's knowledge. A bot is controlled remotely by the attacker under a Command and Control structure. Recent statistics show that bots represent one of the fastest growing threats to our network by performing malicious activities such as email spamming or keylogging. However, few bot detection techniques have been developed to date. In this paper, we investigate a behavioural algorithm to detect a single bot that uses keylogging activity. Our approach involves the use of function calls analysis for the detection of the bot with a keylogging component. Correlation of the frequency of a specified time-window is performed to enhance he detection scheme. We perform a range of experiments with the spybot. Our results show that there is a high correlation between some function calls executed by this bot which indicates abnormal activity in our system.


Understanding Formulation of Social Capital in Online Social Network Sites (SNS)

Abstract: Online communities are the gatherings of like-minded people, brought together in cyberspace by shared interests. The shared interest has hidden social capital aspects and can be of bridging or bonding type. Creating such communities is not a big challenge but sustaining member's participation is. This study examines the formation and maintenance of social capital in social network sites. In addition to assessing bonding and bridging social capital, we explore a dimension of social capital that assesses one's ability to stay connected with members of a previously inhabited community, which we call maintained social capital. Such dimension is enacted here in terms of Hypothesis.


The Influence of Intensity Standardization on Medical Image Registration

Abstract: Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. This phenomenon has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the influence of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images.


Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images

Abstract: This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.


Mod/Resc Parsimony Inference

Abstract: We address in this paper a new computational biology problem that aims at understanding a mechanism that could potentially be used to genetically manipulate natural insect populations infected by inherited, intra-cellular parasitic bacteria. In this problem, that we denote by \textsc{Mod/Resc Parsimony Inference}, we are given a boolean matrix and the goal is to find two other boolean matrices with a minimum number of columns such that an appropriately defined operation on these matrices gives back the input. We show that this is formally equivalent to the \textsc{Bipartite Biclique Edge Cover} problem and derive some complexity results for our problem using this equivalence. We provide a new, fixed-parameter tractability approach for solving both that slightly improves upon a previously published algorithm for the \textsc{Bipartite Biclique Edge Cover}. Finally, we present experimental results where we applied some of our techniques to a real-life data set.


Pure Nash Equilibria: Complete Characterization of Hard and Easy Graphical Games

Abstract: We consider the computational complexity of pure Nash equilibria in graphical games. It is known that the problem is NP-complete in general, but tractable (i.e., in P) for special classes of graphs such as those with bounded treewidth. It is then natural to ask: is it possible to characterize all tractable classes of graphs for this problem? In this work, we provide such a characterization for the case of bounded in-degree graphs, thereby resolving the gap between existing hardness and tractability results. In particular, we analyze the complexity of PUREGG(C, -), the problem of deciding the existence of pure Nash equilibria in graphical games whose underlying graphs are restricted to class C. We prove that, under reasonable complexity theoretic assumptions, for every recursively enumerable class C of directed graphs with bounded in-degree, PUREGG(C, -) is in polynomial time if and only if the reduced graphs (the graphs resulting from iterated removal of sinks) of C have bounded treewidth. We also give a characterization for PURECHG(C,-), the problem of deciding the existence of pure Nash equilibria in colored hypergraphical games, a game representation that can express the additional structure that some of the players have identical local utility functions. We show that the tractable classes of bounded-arity colored hypergraphical games are precisely those whose reduced graphs have bounded treewidth modulo homomorphic equivalence. Our proofs make novel use of Grohe's characterization of the complexity of homomorphism problems.


Proceedings Second International Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software

Abstract: The Second International Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software (PLACES) was co-located with ETAPS 2009 in the city of York, England. The workshop took place on Sunday 22nd March 2009. The workshop focused on the challenges raised by the changing landscape of computer software. Traditionally, most software was written for a single computer with one CPU. However applications on the web today are built using numerous interacting services deployed on across many machines; soon off-the-shelf CPUs will host thousands of cores, and sensor networks will be composed from a large number of processing units. Many normal applications will soon need to make effective use of thousands of computing nodes. At some level of granularity, computation in such systems will be inherently concurrent and communication-centred. The development of effective programming methodologies for the coming computing paradigm demands exploration and understanding of a wide variety of ideas and techniques. This workshop offered a forum where researchers from different fields could exchange new ideas on one of the central challenges for programming in the near future, the development of programming methodologies and infrastructures where concurrency and distribution are the norm rather than a marginal concern.


Integrating Interval Constraints into Logic Programming

Abstract: The CLP scheme uses Horn clauses and SLD resolution to generate multiple constraint satisfaction problems (CSPs). The possible CSPs include rational trees (giving Prolog) and numerical algorithms for solving linear equations and linear programs (giving CLP(R)). In this paper we develop a form of CSP for interval constraints. In this way one obtains a logic semantics for the efficient floating-point hardware that is available on most computers. The need for the method arises because in the practice of scheduling and engineering design it is not enough to solve a single CSP. Ideally one should be able to consider thousands of CSPs and efficiently solve them or show them to be unsolvable. This is what CLP/NCSP, the new subscheme of CLP described in this paper is designed to do.


On Functionality of Visibly Pushdown Transducers

Abstract: Visibly pushdown transducers form a subclass of pushdown transducers that (strictly) extends finite state transducers with a stack. Like visibly pushdown automata, the input symbols determine the stack operations. In this paper, we prove that functionality is decidable in PSpace for visibly pushdown transducers. The proof is done via a pumping argument: if a word with two outputs has a sufficiently large nesting depth, there exists a nested word with two outputs whose nesting depth is strictly smaller. The proof uses technics of word combinatorics. As a consequence of decidability of functionality, we also show that equivalence of functional visibly pushdown transducers is Exptime-Complete.


Iterated Regret Minimization in Game Graphs

Abstract: Iterated regret minimization has been introduced recently by J.Y. Halpern and R. Pass in classical strategic games. For many games of interest, this new solution concept provides solutions that are judged more reasonable than solutions offered by traditional game concepts -- such as Nash equilibrium --. Although computing iterated regret on explicit matrix game is conceptually and computationally easy, nothing is known about computing the iterated regret on games whose matrices are defined implicitly using game tree, game DAG or, more generally game graphs. In this paper, we investigate iterated regret minimization for infinite duration two-player quantitative non-zero sum games played on graphs. We consider reachability objectives that are not necessarily antagonist. Edges are weighted by integers -- one for each player --, and the payoffs are defined by the sum of the weights along the paths. Depending on the class of graphs, we give either polynomial or pseudo-polynomial time algorithms to compute a strategy that minimizes the regret for a fixed player. We finally give algorithms to compute the strategies of the two players that minimize the iterated regret for trees, and for graphs with strictly positive weights only.


Parikh Images of Regular Languages: Complexity and Applications

Abstract: We show that the Parikh image of the language of an NFA with n states over an alphabet of size k can be described as a finite union of linear sets with at most k generators and total size 2^{O(k^2 log n)}, i.e., polynomial for all fixed k >= 1. Previously, it was not known whether the number of generators could be made independent of n, and best upper bounds on the total size were exponential in n. Furthermore, we give an algorithm for performing such a translation in time 2^{O(k^2 log(kn))}. Our proof exploits a previously unknown connection to the theory of convex sets, and establishes a normal form theorem for semilinear sets, which is of independent interests. To complement these results, we show that our upper bounds are tight and that the results cannot be extended to context-free languages. We give four applications: (1) a new polynomial fragment of integer programming, (2) precise complexity of membership for Parikh images of NFAs, (3) an answer to an open question about polynomial PAC-learnability of semilinear sets, and (4) an optimal algorithm for LTL model checking over discrete-timed reversal-bounded counter systems.


A Minimum Relative Entropy Controller for Undiscounted Markov Decision Processes

Abstract: Adaptive control problems are notoriously difficult to solve even in the presence of plant-specific controllers. One way to by-pass the intractable computation of the optimal policy is to restate the adaptive control as the minimization of the relative entropy of a controller that ignores the true plant dynamics from an informed controller. The solution is given by the Bayesian control rule-a set of equations characterizing a stochastic adaptive controller for the class of possible plant dynamics. Here, the Bayesian control rule is applied to derive BCR-MDP, a controller to solve undiscounted Markov decision processes with finite state and action spaces and unknown dynamics. In particular, we derive a non-parametric conjugate prior distribution over the policy space that encapsulates the agent's whole relevant history and we present a Gibbs sampler to draw random policies from this distribution. Preliminary results show that BCR-MDP successfully avoids sub-optimal limit cycles due to its built-in mechanism to balance exploration versus exploitation.


Deterministic Black-Box Identity Testing $\pi$-Ordered Algebraic Branching Programs

Abstract: In this paper we study algebraic branching programs (ABPs) with restrictions on the order and the number of reads of variables in the program. Given a permutation $\pi$ of $n$ variables, for a $\pi$-ordered ABP ($\pi$-OABP), for any directed path $p$ from source to sink, a variable can appear at most once on $p$, and the order in which variables appear on $p$ must respect $\pi$. An ABP $A$ is said to be of read $r$, if any variable appears at most $r$ times in $A$. Our main result pertains to the identity testing problem. Over any field $F$ and in the black-box model, i.e. given only query access to the polynomial, we have the following result: read $r$ $\pi$-OABP computable polynomials can be tested in $\DTIME[2^{O(r\log r \cdot \log^2 n \log\log n)}]$. Our next set of results investigates the computational limitations of OABPs. It is shown that any OABP computing the determinant or permanent requires size $\Omega(2^n/n)$ and read $\Omega(2^n/n^2)$. We give a multilinear polynomial $p$ in $2n+1$ variables over some specifically selected field $G$, such that any OABP computing $p$ must read some variable at least $2^n$ times. We show that the elementary symmetric polynomial of degree $r$ in $n$ variables can be computed by a size $O(rn)$ read $r$ OABP, but not by a read $(r-1)$ OABP, for any $0 < 2r-1 \leq n$. Finally, we give an example of a polynomial $p$ and two variables orders $\pi \neq \pi'$, such that $p$ can be computed by a read-once $\pi$-OABP, but where any $\pi'$-OABP computing $p$ must read some variable at least $2^n$


Extensible type checker for parser generation

Abstract: Parser generators generate translators from language specifications. In many cases, such specifications contain semantic actions written in the same language as the generated code. Since these actions are subject to little static checking, they are usually a source of errors which are discovered only when generated code is compiled. In this paper we propose a parser generator front-end which statically checks semantic actions for typing errors and prevents such errors from appearing in generated code. The type checking procedure is extensible to support many implementation languages. An extension for Java is presented along with an extension for declarative type system descriptions.


Max-min Fairness in 802.11 Mesh Networks

Abstract: In this paper we build upon the recent observation that the 802.11 rate region is log-convex and, for the first time, characterise max-min fair rate allocations for a large class of 802.11 wireless mesh networks. By exploiting features of the 802.11e/n MAC, in particular TXOP packet bursting, we are able to use this characterisation to establish a straightforward, practically implementable approach for achieving max-min throughput fairness. We demonstrate that this approach can be readily extended to encompass time-based fairness in multi-rate 802.11 mesh networks.


Derandomized Parallel Repetition via Structured PCPs

Abstract: A PCP is a proof system for NP in which the proof can be checked by a probabilistic verifier. The verifier is only allowed to read a very small portion of the proof, and in return is allowed to err with some bounded probability. The probability that the verifier accepts a false proof is called the soundness error, and is an important parameter of a PCP system that one seeks to minimize. Constructing PCPs with sub-constant soundness error and, at the same time, a minimal number of queries into the proof (namely two) is especially important due to applications for inapproximability. In this work we construct such PCP verifiers, i.e., PCPs that make only two queries and have sub-constant soundness error. Our construction can be viewed as a combinatorial alternative to the "manifold vs. point" construction, which is the only construction in the literature for this parameter range. The "manifold vs. point" PCP is based on a low degree test, while our construction is based on a direct product test. We also extend our construction to yield a decodable PCP (dPCP) with the same parameters. By plugging in this dPCP into the scheme of Dinur and Harsha (FOCS 2009) one gets an alternative construction of the result of Moshkovitz and Raz (FOCS 2008), namely: a construction of two-query PCPs with small soundness error and small alphabet size. Our construction of a PCP is based on extending the derandomized direct product test of Impagliazzo, Kabanets and Wigderson (STOC 09) to a derandomized parallel repetition theorem. More accurately, our PCP construction is obtained in two steps. We first prove a derandomized parallel repetition theorem for specially structured PCPs. Then, we show that any PCP can be transformed into one that has the required structure, by embedding it on a de-Bruijn graph.


Algebraic Linear Orderings

Abstract: An algebraic linear ordering is a component of the initial solution of a first-order recursion scheme over the continuous categorical algebra of countable linear orderings equipped with the sum operation and the constant 1. Due to a general Mezei-Wright type result, algebraic linear orderings are exactly those isomorphic to the linear ordering of the leaves of an algebraic tree. Using Courcelle's characterization of algebraic trees, we obtain the fact that a linear ordering is algebraic if and only if it can be represented as the lexicographic ordering of a deterministic context-free language. When the algebraic linear ordering is a well-ordering, its order type is an algebraic ordinal. We prove that the Hausdorff rank of any scattered algebraic linear ordering is less than $\omega^\omega$. It follows that the algebraic ordinals are exactly those less than $\omega^{\omega^\omega}$.


Non Uniform Selection of Solutions for Upper Bounding the 3-SAT Threshold

Abstract: We give a new insight into the upper bounding of the 3-SAT threshold by the first moment method. The best criteria developed so far to select the solutions to be counted discriminate among neighboring solutions on the basis of uniform information about each individual free variable. What we mean by uniform information, is information which does not depend on the solution: e.g. the number of positive/negative occurrences of the considered variable. What is new in our approach is that we use non uniform information about variables. Thus we are able to make a more precise tuning, resulting in a slight improvement on upper bounding the 3-SAT threshold for various models of formulas defined by their distributions.


Scenario Based Worm Trace Pattern Identification Technique

Abstract: The number of malware variants is growing tremendously and the study of malware attacks on the Internet is still a demanding research domain. In this research, various logs from different OSI layer are explore to identify the traces leave on the attacker and victim logs, and the attack worm trace pattern are establish in order to reveal true attacker or victim. For the purpose of this paper, it will only concentrate on cybercrime that caused by malware network intrusion and used the traditional worm namely blaster worm variants. This research creates the concept of trace pattern by fusing the attackers and victims perspective. Therefore, the objective of this paper is to propose on attackers, victims and multistep, attacker or victim, trace patterns by combining both perspectives. These three proposed worm trace patterns can be extended into research areas in alert correlation and computer forensic investigation.


Avoiding Black Hole and Cooperative Black Hole Attacks in Wireless Ad hoc Networks

Abstract: In wireless ad hoc networks, the absence of any control on packets forwarding, make these networks vulnerable by various deny of service attacks (DoS). A node, in wireless ad hoc network, counts always on intermediate nodes to send these packets to a given destination node. An intermediate node, which takes part in packets forwarding, may behave maliciously and drop packets which goes through it, instead of forwarding them to the following node. Such behavior is called black hole attack. In this paper, after having specified the black hole attack, a secure mechanism, which consists in checking the good forwarding of packets by an intermediate node, was proposed. The proposed solution avoids the black hole and the cooperative black hole attacks. Evaluation metrics were considered in simulation to show the effectiveness of the suggested solution.


Design of Current Controller for Two Quadrant DC Motor Drive by Using Model Order Reduction Technique

Abstract: In this paper, design of current controller for a two quadrant DC motor drive was proposed with the help of model order reduction technique. The calculation of current controller gain with some approximations in the conventional design process is replaced by proposed model order reduction method. The model order reduction technique proposed in this paper gives the better controller gain value for the DC motor drive. The proposed model order reduction method is a mixed method, where the numerator polynomial of reduced order model is obtained by using stability equation method and the denominator polynomial is obtained by using some approximation technique preceded in this paper. The designed controllers responses were simulated with the help of MATLAB to show the validity of the proposed method.


Wireless Congestion Control Protocol For Multihop Ad Hoc Networks

Abstract: The traditional TCP congestion control mechanism encounters a number of new problems and suffers a poor performance when the IEEE 802.11 MAC protocol is used in multihop ad hoc networks. Many of the problems result from medium contention at the MAC layer. In this paper, I first illustrate that severe medium contention and congestion are intimately coupled, and TCP s congestion control algorithm becomes too coarse in its granularity, causing throughput instability and excessively long delay. Further, we illustrate TCP s severe unfairness problem due to the medium contention and the tradeoff between aggregate throughput and fairness. Then, based on the novel use of channel busyness ratio, a more accurate metric to characterize the network utilization and congestion status, I propose a new wireless congestion control protocol (WCCP) to efficiently and fairly support the transport service in multihop ad hoc networks. In this protocol, each forwarding node along a traffic flow exercises the internode and intranode fair resource allocation and determines the MAC layer feedback accordingly. The endtoend feedback, which is ultimately determined by the bottleneck node along the flow, is carried back to the source to control its sending rate. Extensive simulations show that WCCP significantly outperforms traditional TCP in terms of channel utilization, delay, and fairness, and eliminates the starvation problem.


Saturation Throughput Analysis of IEEE 802.11b Wireless Local Area Networks under High Interference Considering Capture Effects

Abstract: Distributed contention based Medium Access Control (MAC) protocols are the fundamental components for IEEE 802.11 based Wireless Local Area Networks (WLANs). Contention windows (CW) change dynamically to adapt to the current contention level, Upon each packet collision, a station doubles its CW to reduce further collision of packets. IEEE 802.11 Distributed Coordination Function (DCF) suffers from a common problem in erroneous channel. They cannot distinguish noise lost packets from collision lost packets. In both situations a station does not receive its ACK and doubles the CW to reduce further packet collisions. This increases backoff overhead unnecessarily in addition to the noise lost packets, reduces the throughput significantly. Furthermore, the aggregate throughput of a practical WLAN strongly depends on the channel conditions. In real radio environment, the received signal power at the access point from a station is subjected to deterministic path loss, shadowing and fast multipath fading. In this paper, we propose a new saturation throughput analysis for IEEE 802.11 DCF considering erroneous channel and capture effects. To alleviate the low performance of IEEE 802.11 DCF, we introduce a mechanism that greatly outperforms under noisy environment with low network traffic and compare their performances to the existing standards. We extend the multidimensional Markov chain model initially proposed by Bianchi(3) to characterize the behavior of DCF in order to account both real channel conditions and capture effects, especially in a high interference radio environment.


Performance Evaluation of Unicast and Broadcast Mobile Ad hoc Network Routing Protocols

Abstract: Efficient routing mechanism is a challenging issue for group oriented computing in Mobile Ad Hoc Networks (MANETs). The ability of MANETs to support adequate Quality of Service (QoS) for group communication is limited by the ability of the underlying ad-hoc routing protocols to provide consistent behavior despite the dynamic properties of mobile computing devices. In MANET QoS requirements can be quantified in terms of Packet Delivery Ratio (PDR), Data Latency, Packet Loss Probability, Routing Overhead, Medium Access Control (MAC) Overhead and Data Throughput etc. This paper presents an in depth study of one to many and many to many communications in MANETs and provides a comparative performance evaluation of unicast and broadcast routing protocols. Dynamic Source Routing protocol (DSR) is used as unicast protocol and BCAST is used to represent broadcast protocol. The performance differentials are analyzed using ns2 network simulator varying multicast group size (number of data senders and data receivers). Both protocols are simulated with identical traffic loads and mobility models. Simulation result shows that BCAST performs better than DSR in most cases.


The Importance Analysis of Use Case Map with Markov Chains

Abstract: UCMs (Use Case Maps) model describes functional requirements and high-level designs with causal paths superimposed on a structure of components. It could provide useful resources for software acceptance testing. However until now statistical testing technologies for large scale software is not considered yet in UCMs model. Thus if one applies UCMs model to a large scale software using traditional coverage based exhaustive tasting, then it requires too much costs for the quality assurance. Therefore this paper proposes an importance analysis of UCMs model with Markov chains. With this approach not only highly frequently used usage scenarios but also important objects such as components, responsibilities, stubs and plugins can also be identified from UCMs specifications. Therefore careful analysis, design, implementation and efficient testing could be possible with the importance of scenarios and objects during the full software life cycle. Consequently product reliability can be obtained with low costs. This paper includes an importance analysis method that identifies important scenarios and objects and a case study to illustrate the applicability of the proposed approach.


An Approximate Subgame-Perfect Equilibrium Computation Technique for Repeated Games

Abstract: This paper presents a technique for approximating, up to any precision, the set of subgame-perfect equilibria (SPE) in discounted repeated games. The process starts with a single hypercube approximation of the set of SPE. Then the initial hypercube is gradually partitioned on to a set of smaller adjacent hypercubes, while those hypercubes that cannot contain any point belonging to the set of SPE are simultaneously withdrawn. Whether a given hypercube can contain an equilibrium point is verified by an appropriate mathematical program. Three different formulations of the algorithm for both approximately computing the set of SPE payoffs and extracting players' strategies are then proposed: the first two that do not assume the presence of an external coordination between players, and the third one that assumes a certain level of coordination during game play for convexifying the set of continuation payoffs after any repeated game history. A special attention is paid to the question of extracting players' strategies and their representability in form of finite automata, an important feature for artificial agent systems.


An Improved DC Recovery Method from AC Coefficients of DCT-Transformed Images

Abstract: Motivated by the work of Uehara et al. [1], an improved method to recover DC coefficients from AC coefficients of DCT-transformed images is investigated in this work, which finds applications in cryptanalysis of selective multimedia encryption. The proposed under/over-flow rate minimization (FRM) method employs an optimization process to get a statistically more accurate estimation of unknown DC coefficients, thus achieving a better recovery performance. It was shown by experimental results based on 200 test images that the proposed DC recovery method significantly improves the quality of most recovered images in terms of the PSNR values and several state-of-the-art objective image quality assessment (IQA) metrics such as SSIM and MS-SSIM.


Estimating and Sampling Graphs with Multidimensional Random Walks

Abstract: Estimating characteristics of large graphs via sampling is a vital part of the study of complex networks. Current sampling methods such as (independent) random vertex and random walks are useful but have drawbacks. Random vertex sampling may require too many resources (time, bandwidth, or money). Random walks, which normally require fewer resources per sample, can suffer from large estimation errors in the presence of disconnected or loosely connected graphs. In this work we propose a new $m$-dimensional random walk that uses $m$ dependent random walkers. We show that the proposed sampling method, which we call Frontier sampling, exhibits all of the nice sampling properties of a regular random walk. At the same time, our simulations over large real world graphs show that, in the presence of disconnected or loosely connected components, Frontier sampling exhibits lower estimation errors than regular random walks. We also show that Frontier sampling is more suitable than random vertex sampling to sample the tail of the degree distribution of the graph.


Cuspidal and Noncuspidal Robot Manipulators

Abstract: This article synthezises the most important results on the kinematics of cuspidal manipulators i.e. nonredundant manipulators that can change posture without meeting a singularity. The characteristic surfaces, the uniqueness domains and the regions of feasible paths in the workspace are defined. Then, several sufficient geometric conditions for a manipulator to be noncuspidal are enumerated and a general necessary and sufficient condition for a manipulator to be cuspidal is provided. An explicit DH-parameter-based condition for an orthogonal manipulator to be cuspidal is derived. The full classification of 3R orthogonal manipulators is provided and all types of cuspidal and noncuspidal orthogonal manipulators are enumerated. Finally, some facts about cuspidal and noncuspidal 6R manipulators are reported.


Position Analysis of the RRP-3(SS) Multi-Loop Spatial Structure

Abstract: The paper presents the position analysis of a spatial structure composed of two platforms mutually connected by one RRP and three SS serial kinematic chains, where R, P, and S stand for revolute, prismatic, and spherical kinematic pair respectively. A set of three compatibility equations is laid down that, following algebraic elimination, results in a 28th-order univariate algebraic equation, which in turn provides the addressed problem with 28 solutions in the complex domain. Among the applications of the results presented in this paper is the solution to the forward kinematics of the Tricept, a well-known in-parallel-actuated spatial manipulator. Numerical examples show adoption of the proposed method in dealing with two case studies.


Online Distributed Sensor Selection

Abstract: A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore for large sensor networks solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In this paper, we present Distributed Online Greedy (DOG), an efficient, distributed algorithm for repeatedly selecting sensors online, only receiving feedback about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (unknown) utility function satisfies a natural diminishing returns property called submodularity. Our algorithm has extremely low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks.


Refinement and Verification of Real-Time Systems

Abstract: This paper discusses highly general mechanisms for specifying the refinement of a real-time system as a collection of lower level parallel components that preserve the timing and functional requirements of the upper level specification. These mechanisms are discussed in the context of ASTRAL, which is a formal specification language for real-time systems. Refinement is accomplished by mapping all of the elements of an upper level specification into lower level elements that may be split among several parallel components. In addition, actions that can occur in the upper level are mapped to actions of components operating at the lower level. This allows several types of implementation strategies to be specified in a natural way, while the price for generality (in terms of complexity) is paid only when necessary. The refinement mechanisms are first illustrated using a simple digital circuit; then, through a highly complex phone system; finally, design guidelines gleaned from these specifications are presented.


The median of the distance between two leaves in a phylogenetic tree

Abstract: We establish a limit formula for the median of the distance between two leaves in a fully resolved unrooted phylogenetic tree with n leaves. More precisely, we prove that this median is equal, in the limit, to the square root of 4*ln(2)*n.


The Full Abstraction Problem for Higher Order Functional-Logic Programs

Abstract: Developing suitable formal semantics can be of great help in the understanding, design and implementation of a programming language, and act as a guide for software development tools like analyzers or partial evaluators. In this sense, full abstraction is a highly desirable property, indicating a perfect correspondence between the semantics and the observable behavior of program pieces. In this work we address the question of full abstraction for the family of modern functional logic languages, in which functions can be higher order and non-deterministic, and where the semantics adopted for non-determinism is \emph{call-time choice}. We show that, with respect to natural notions of \emph{observation}, any semantics based on \emph{extensional} functions is necessarily unsound; in contrast, we show that the higher order version of \emph{CRWL}, a well-known existing semantic framework for functional logic programming, based on an \emph{intensional} view of functions, turns out to be fully abstract and compositional.


AROMA: Automatic Generation of Radio Maps for Localization Systems

Abstract: WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.


Towards Parameterized Regular Type Inference Using Set Constraints

Abstract: We propose a method for inferring \emph{parameterized regular types} for logic programs as solutions for systems of constraints over sets of finite ground Herbrand terms (set constraint systems). Such parameterized regular types generalize \emph{parametric} regular types by extending the scope of the parameters in the type definitions so that such parameters can relate the types of different predicates. We propose a number of enhancements to the procedure for solving the constraint systems that improve the precision of the type descriptions inferred. The resulting algorithm, together with a procedure to establish a set constraint system from a logic program, yields a program analysis that infers tighter safe approximations of the success types of the program than previous comparable work, offering a new and useful efficiency vs. precision trade-off. This is supported by experimental results, which show the feasibility of our analysis.


Recursive tilings and space-filling curves with little fragmentation

Abstract: This paper defines the Arrwwid number of a recursive tiling (or space-filling curve) as the smallest number w such that any ball Q can be covered by w tiles (or curve sections) with total volume O(vol(Q)). Recursive tilings and space-filling curves with low Arrwwid numbers can be applied to optimise disk, memory or server access patterns when processing sets of points in d-dimensional space. This paper presents recursive tilings and space-filling curves with optimal Arrwwid numbers. For d >= 3, we see that regular cube tilings and space-filling curves cannot have optimal Arrwwid number, and we see how to construct alternatives with better Arrwwid numbers.


Mobility Impact on Performance of Mobile Grids

Abstract: Wireless mobile grids are one of the emerging grid types, which help to pool the resources of several willing and cooperative mobile devices to resolve a computationally intensive task. The mobile grids exhibit stronger challenges like mobility management of devices, providing transparent access to grid resources, task management and handling of limited resources so that resources are shared efficiently. Task execution on these devices should not be affected by their mobility. The proposed work presents performance evaluation of wireless mobile grid using normal walk mobility model. The normal walk model represents daily motion of users and the direction of motion is mostly symmetric in a real life environment, thus it is effective in location updating of a mobile station and in turn helps task distribution among these available mobile stations. Some of the performance parameters such as Task Execution Time, task failure rate, communication overhead on Brokering Server and Monitoring Cost are discussed.


Finding and counting vertex-colored subtrees

Abstract: The problems studied in this article originate from the Graph Motif problem introduced by Lacroix et al. in the context of biological networks. The problem is to decide if a vertex-colored graph has a connected subgraph whose colors equal a given multiset of colors $M$. It is a graph pattern-matching problem variant, where the structure of the occurrence of the pattern is not of interest but the only requirement is the connectedness. Using an algebraic framework recently introduced by Koutis et al., we obtain new FPT algorithms for Graph Motif and variants, with improved running times. We also obtain results on the counting versions of this problem, proving that the counting problem is FPT if M is a set, but becomes W[1]-hard if M is a multiset with two colors. Finally, we present an experimental evaluation of this approach on real datasets, showing that its performance compares favorably with existing software.


Evaluation and Design Space Exploration of a Time-Division Multiplexed NoC on FPGA for Image Analysis Applications

Abstract: The aim of this paper is to present an adaptable Fat Tree NoC architecture for Field Programmable Gate Array (FPGA) designed for image analysis applications. Traditional NoCs (Network on Chip) are not optimal for dataflow applications with large amount of data. On the opposite, point to point communications are designed from the algorithm requirements but they are expensives in terms of resource and wire. We propose a dedicated communication architecture for image analysis algorithms. This communication mechanism is a generic NoC infrastructure dedicated to dataflow image processing applications, mixing circuit-switching and packet-switching communications. The complete architecture integrates two dedicated communication architectures and reusable IP blocks. Communications are based on the NoC concept to support the high bandwidth required for a large number and type of data.


On the equivalence between hierarchical segmentations and ultrametric watersheds

Abstract: We study hierarchical segmentation in the framework of edge-weighted graphs. We define ultrametric watersheds as topological watersheds null on the minima. We prove that there exists a bijection between the set of ultrametric watersheds and the set of hierarchical segmentations. We end this paper by showing how to use the proposed framework in practice in the example of constrained connectivity; in particular it allows to compute such a hierarchy following a classical watershed-based morphological scheme, which provides an efficient algorithm to compute the whole hierarchy.


Adaptive Subcarrier PSK Intensity Modulation in Free Space Optical Systems

Abstract: We propose an adaptive transmission technique for free space optical (FSO) systems, operating in atmospheric turbulence and employing subcarrier phase shift keying (S-PSK) intensity modulation. Exploiting the constant envelope characteristics of S-PSK, the proposed technique offers efficient utilization of the FSO channel capacity by adapting the modulation order of S-PSK, according to the instantaneous state of turbulence induced fading and a pre-defined bit error rate (BER) requirement. Novel expressions for the spectral efficiency and average BER of the proposed adaptive FSO system are presented and performance investigations under various turbulence conditions and target BER requirements are carried out. Numerical results indicate that significant spectral efficiency gains are offered without increasing the transmitted average optical power or sacrificing BER requirements, in moderate-to-strong turbulence conditions. Furthermore, the proposed variable rate transmission technique is applied to multiple input multiple output (MIMO) FSO systems, providing additional improvement in the achieved spectral efficiency as the number of the transmit and/or receive apertures increases.


Thai Rhetorical Structure Analysis

Abstract: Rhetorical structure analysis (RSA) explores discourse relations among elementary discourse units (EDUs) in a text. It is very useful in many text processing tasks employing relationships among EDUs such as text understanding, summarization, and question-answering. Thai language with its distinctive linguistic characteristics requires a unique technique. This article proposes an approach for Thai rhetorical structure analysis. First, EDUs are segmented by two hidden Markov models derived from syntactic rules. A rhetorical structure tree is constructed from a clustering technique with its similarity measure derived from Thai semantic rules. Then, a decision tree whose features derived from the semantic rules is used to determine discourse relations.


On the Minimal Uncompletable Word Problem

Abstract: Let S be a finite set of words over an alphabet Sigma. The set S is said to be complete if every word w over the alphabet Sigma is a factor of some element of S*, i.e. w belongs to Fact(S*). Otherwise if S is not complete, we are interested in finding bounds on the minimal length of words in Sigma* which are not elements of Fact(S*) in terms of the maximal length of words in S.


Making Sense of the Evolution of a Scientific Domain: A Visual Analytic Study of the Sloan Digital Sky Survey Research

Abstract: We introduce a new visual analytic approach to the study of scientific discoveries and knowledge diffusion. Our approach enhances contemporary co-citation network analysis by enabling analysts to identify co-citation clusters of cited references intuitively, synthesize thematic contexts in which these clusters are cited, and trace how research focus evolves over time. The new approach integrates and streamlines a few previously isolated techniques such as spectral clustering and feature selection algorithms. The integrative procedure is expected to empower and strengthen analytical and sense making capabilities of scientists, learners, and researchers to understand the dynamics of the evolution of scientific domains in a wide range of scientific fields, science studies, and science policy evaluation and planning. We demonstrate the potential of our approach through a visual analysis of the evolution of astronomical research associated with the Sloan Digital Sky Survey (SDSS) using bibliographic data between 1994 and 2008. In addition, we also demonstrate that the approach can be consistently applied to a set of heterogeneous data sources such as e-prints on arXiv, publications on ADS, and NSF awards related to the same topic of SDSS.


Persistence Diagrams and the Heat Equation Homotopy

Abstract: Persistence homology is a tool used to measure topological features that are present in data sets and functions. Persistence pairs births and deaths of these features as we iterate through the sublevel sets of the data or function of interest. I am concerned with using persistence to characterize the difference between two functions f, g : M -> R, where M is a topological space. Furthermore, I formulate a homotopy from g to f by applying the heat equation to the difference function g-f. By stacking the persistence diagrams associated with this homotopy, we create a vineyard of curves that connect the points in the diagram for f with the points in the diagram for g. I look at the diagrams where M is a square, a sphere, a torus, and a Klein bottle. Looking at these four topologies, we notice trends (and differences) as the persistence diagrams change with respect to time.


Convergence of Corporate and Information Security

Abstract: As physical and information security boundaries have become increasingly blurry many organizations are experiencing challenges with how to effectively and efficiently manage security within the corporate. There is no current standard or best practice offered by the security community regarding convergence; however many organizations such as the Alliance for Enterprise Security Risk Management (AESRM) offer some excellent suggestions for integrating a converged security program. This paper reports on how organizations have traditionally managed asset protection, why that is changing and how to establish convergence to optimize security value to the business within an enterprise.


Image Retrieval Techniques based on Image Features, A State of Art approach for CBIR

Abstract: The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Firstly, this paper outlines a description of the primitive feature extraction techniques like, texture, colour, and shape. Once these features are extracted and used as the basis for a similarity check between images, the various matching techniques are discussed. Furthermore, the results of its performance are illustrated by a detailed example.


Ahb Compatible DDR Sdram Controller Ip Core for Arm Based Soc

Abstract: DDR SDRAM is similar in function to the regular SDRAM but doubles the bandwidth of the memory by transferring data on both edges of the clock cycles. DDR SDRAM most commonly used in various embedded application like networking, image or video processing, Laptops ete. Now a days many applications needs more and more cheap and fast memory. Especially in the field of signal processing, requires significant amount of memory. The most used type of dynamic memory for that purpose is DDR SDRAM. For FPGA design the IC manufacturers are providing commercial memory controller IP cores working only on their products. Main disadvantage is the lack of memory access optimization for random memory access patterns. The data path part of those controllers can be used free of charge. This work propose an architecture of a DDR SDRAM controller, which takes advantage of those available and well tested data paths and can be used for any FPGA device or ASIC design.(5). In most of the SOC design, DDR SDRAM is commonly used. ARM processor is widely used in SOCs; so that we focused to implement AHB compatible DDR SDRAM controller suitable for ARM based SOC design.


High Throughput of WiMAX MIMO OFDM Including Adaptive Modulation and Coding

Abstract: WiMAX technology is based on the IEEE 802.16 specification of which IEEE 802.16-2004 and 802.16e amendment are Physical (PHY) layer specifications. IEEE 802.16-2004 currently supports several multiple-antenna options including Space-Time Codes (STC), Multiple-Input Multiple-Output (MIMO) antenna systems and Adaptive Antenna Systems (AAS). The most recent WiMAX standard (802.16e) supports broadband applications to mobile terminals and laptops. Using Adaptive Modulation and Coding (AMC) we analyze the performance of OFDM physical layer in WiMAX based on the simulation results of Bit Error Rate (BER), and data throughput. The performance analysis of OFDM PHY is done. In this paper, an extension to the basic SISO mode, a number of 2 by 2 MIMO extensions are analysed under different combinations of digital modulation (QPSK, 16QAM and 64QAM) and Convolutional Code (CC) with half, two-third and three quarter rated codes. The intent of this paper is to provide an idea of the benefits of multiple antenna systems over single antenna systems in WiMAX type deployments.


Performance Modeling and Evaluation of Traffic management for Mobile Networks by SINR Prediction

Abstract: Over the recent years a considerable amount of effort has been devoted towards the performance evaluation and prediction of Mobile Networks. Performance modeling and evaluation of mobile networks are very important in view of their ever expending usage and the multiplicity of their component parts together with the complexity of their functioning. The present paper addresses current issues in traffic management and congestion control by (signal to interference plus noise ratio) SINR prediction congestion control, routing and optimization of cellular mobile networks.


The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

Abstract: A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks.


Implementing Genetic Algorithms on Arduino Micro-Controllers

Abstract: Since their conception in 1975, Genetic Algorithms have been an extremely popular approach to find exact or approximate solutions to optimization and search problems. Over the last years there has been an enhanced interest in the field with related techniques, such as grammatical evolution, being developed. Unfortunately, work on developing genetic optimizations for low-end embedded architectures hasn't embraced the same enthusiasm. This short paper tackles that situation by demonstrating how genetic algorithms can be implemented in Arduino Duemilanove, a 16 MHz open-source micro-controller, with limited computation power and storage resources. As part of this short paper, the libraries used in this implementation are released into the public domain under a GPL license.


Dire n'est pas concevoir

Abstract: The conceptual modelling built from text is rarely an ontology. As a matter of fact, such a conceptualization is corpus-dependent and does not offer the main properties we expect from ontology. Furthermore, ontology extracted from text in general does not match ontology defined by expert using a formal language. It is not surprising since ontology is an extra-linguistic conceptualization whereas knowledge extracted from text is the concern of textual linguistics. Incompleteness of text and using rhetorical figures, like ellipsis, modify the perception of the conceptualization we may have. Ontological knowledge, which is necessary for text understanding, is not in general embedded into documents.


On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers

Abstract: Recently Kutin and Niyogi investigated several notions of algorithmic stability--a property of a learning map conceptually similar to continuity--showing that training-stability is sufficient for consistency of Empirical Risk Minimization while distribution-free CV-stability is necessary and sufficient for having finite VC-dimension. This paper concerns a phase transition in the training stability of ERM, conjectured by the same authors. Kutin and Niyogi proved that ERM on finite hypothesis spaces containing a unique risk minimizer has training stability that scales exponentially with sample size, and conjectured that the existence of multiple risk minimizers prevents even super-quadratic convergence. We prove this result for the strictly weaker notion of CV-stability, positively resolving the conjecture.


Intrinsic dimension estimation of data by principal component analysis

Abstract: Estimating intrinsic dimensionality of data is a classic problem in pattern recognition and statistics. Principal Component Analysis (PCA) is a powerful tool in discovering dimensionality of data sets with a linear structure; it, however, becomes ineffective when data have a nonlinear structure. In this paper, we propose a new PCA-based method to estimate intrinsic dimension of data with nonlinear structures. Our method works by first finding a minimal cover of the data set, then performing PCA locally on each subset in the cover and finally giving the estimation result by checking up the data variance on all small neighborhood regions. The proposed method utilizes the whole data set to estimate its intrinsic dimension and is convenient for incremental learning. In addition, our new PCA procedure can filter out noise in data and converge to a stable estimation with the neighborhood region size increasing. Experiments on synthetic and real world data sets show effectiveness of the proposed method.


A Sublogarithmic Approximation for Highway and Tollbooth Pricing

Abstract: An instance of the tollbooth problem consists of an undirected network and a collection of single-minded customers, each of which is interested in purchasing a fixed path subject to an individual budget constraint. The objective is to assign a per-unit price to each edge in a way that maximizes the collective revenue obtained from all customers. The revenue generated by any customer is equal to the overall price of the edges in her desired path, when this cost falls within her budget; otherwise, that customer will not purchase any edge. Our main result is a deterministic algorithm for the tollbooth problem on trees whose approximation ratio is O(log m / log log m), where m denotes the number of edges in the underlying graph. This finding improves on the currently best performance guarantees for trees, due to Elbassioni et al. (SAGT '09), as well as for paths (commonly known as the highway problem), due to Balcan and Blum (EC '06). An additional interesting consequence is a computational separation between tollbooth pricing on trees and the original prototype problem of single-minded unlimited supply pricing, under a plausible hardness hypothesis due to Demaine et al. (SODA '06).


Reaching the Unreached A Role of ICT in Sustainable Rural Development

Abstract: We have seen in last few decades that the progress of information technology with leaps and bounds, which have completely changed the way of life in the developed nations. While internet has changed the established working practice and opened new vistas and provided a platform to connect, this gives the opportunity for collaborative work space that goes beyond the global boundary. ICT promises a fundamental change in all aspects of our lives, including knowledge dissemination, social interaction, economic and business practices, political engagement, media, education, health, leisure and entertainment...This paper introduces the application of ICT for rural development. The paper aims at improving the delivery of information to rural masses such as, technology information, marketing information, and information advice. This paper focuses digital divide and poverty eradication, good governance and the significance of internet for rural development. The paper concludes that ICTs offer the developing country, the opportunity to look ahead several stages of rural development by the use of internet. Effective use of ICT can demolish geographical boundaries and can bring rural communities closer to global economic systems and be of meaningful help to the underprivileged.


Optimization with More than One Budget

Abstract: A natural way to deal with multiple, partially conflicting objectives is turning all the objectives but one into budget constraints. Some classical polynomial-time optimization problems, such as spanning tree and forest, shortest path, (perfect) matching, independent set (basis) in a matroid or in the intersection of two matroids, become NP-hard even with one budget constraint. Still, for most of these problems deterministic and randomized polynomial-time approximation schemes are known. In the case of two or more budgets, typically only multi-criteria approximation schemes are available, which return slightly infeasible solutions. Not much is known however for the case of strict budget constraints: filling this gap is the main goal of this paper. We show that shortest path, perfect matching, and spanning tree (and hence matroid basis and matroid intersection basis) are inapproximable already with two budget constraints. For the remaining problems, whose set of solutions forms an independence system, we present deterministic and randomized polynomial-time approximation schemes for a constant number k of budget constraints. Our results are based on a variety of techniques: 1. We present a simple and powerful mechanism to transform multi-criteria approximation schemes into pure approximation schemes. 2. We show that points in low dimensional faces of any matroid polytope are almost integral, an interesting result on its own. This gives a deterministic approximation scheme for k-budgeted matroid independent set. 3. We present a deterministic approximation scheme for 2-budgeted matching. The backbone of this result is a purely topological property of curves in R^2.


Partial monoids: associativity and confluence

Abstract: A partial monoid $P$ is a set with a partial multiplication $\times$ (and total identity $1_P$) which satisfies some associativity axiom. The partial monoid $P$ may be embedded in a free monoid $P^*$ and the product $\star$ is simulated by a string rewriting system on $P^*$ that consists in evaluating the concatenation of two letters as a product in $P$, when it is defined, and a letter $1_P$ as the empty word $\epsilon$. In this paper we study the profound relations between confluence for such a system and associativity of the multiplication. Moreover we develop a reduction strategy to ensure confluence and which allows us to define a multiplication on normal forms associative up to a given congruence of $P^*$. Finally we show that this operation is associative if, and only if, the rewriting system under consideration is confluent.


Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering

Abstract: Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to improve the diagnostic accuracy. In this paper, we have proposed an algorithm for detecting microcalcification in mammogram. The proposed microcalcification detection algorithm involves mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microcalcification detection by fuzzy shell clustering. It may be possible to detect nodular components such as microcalcification accurately by introducing shape information. The effectiveness of the proposed algorithm for microcalcification detection is confirmed by experimental results.


The Fast Haar Wavelet Transform for Signal & Image Processing

Abstract: A method for the design of Fast Haar wavelet for signal processing and image processing has been proposed. In the proposed work, the analysis bank and synthesis bank of Haar wavelet is modified by using polyphase structure. Finally, the Fast Haar wavelet was designed and it satisfies alias free and perfect reconstruction condition. Computational time and computational complexity is reduced in Fast Haar wavelet transform.


A Survivability Strategy in Route Optimization Mobile Network by Memetic Algorithm

Abstract: The capability to provide network service even under a significant network system element disruption is the backbone for the survival of route optimize of mobile network Technology in today s world. Keeping this view in mind, the present paper highlights a new method based on memetic algorithm.


Analysis of Large Scale Propagation Models for Mobile Communications in Urban Area

Abstract: Channel properties influence the development of wireless communication systems. Unlike wired channels that are stationary and predictable, radio channels are extremely random and dont offer easy analysis. A Radio Propagation Model (RPM), also known as the Radio Wave Propagation Model (RWPM), is an empirical mathematical formulation for the characterization of radio wave propagation as a function of frequency. In mobile radio systems, path loss models are necessary for proper planning, interference estimations, frequency assignments and cell parameters which are the basic for network planning process as well as Location Based Services (LBS) techniques. Propagation models that predict the mean signal strength for an arbitrary transmitter receiver (T R) separation distance which is useful in estimating the radio coverage area of a transmitter are called large scale propagation models, since they characterize signal strength over large TR separation distances. In this paper, the large scale propagation performance of Okumura, Hata, and Lee models has been compared varying Mobile Station (MS) antenna height, Transmitter Receiver (TR) distance and Base Station (BS) antenna height, considering the system to operate at 900 MHz. Through the MATLAB simulation it is turned out that the Okumura model shows the better performance than that of the other large scale propagation models.


Performance Evaluation of TCP over Mobile Ad hoc Networks

Abstract: With the proliferation of mobile computing devices, the demand for continuous network connectivity regardless of physical location has spurred interest in the use of mobile ad hoc networks. Since Transmission Control Protocol (TCP) is the standard network protocol for communication in the internet, any wireless network with Internet service need to be compatible with TCP. TCP is tuned to perform well in traditional wired networks, where packet losses occur mostly because of congestion. However, TCP connections in Ad-hoc mobile networks are plagued by problems such as high bit error rates, frequent route changes, multipath routing and temporary network partitions. The throughput of TCP over such connection is not satisfactory, because TCP misinterprets the packet loss or delay as congestion and invokes congestion control and avoidance algorithm. In this research, the performance of TCP in Adhoc mobile network with high Bit Error rate (BER) and mobility is studied and investigated. Simulation model is implemented and experiments are performed using the Network Simulatior 2 (NS2).


Vision Based Game Development Using Human Computer Interaction

Abstract: A Human Computer Interface (HCI) System for playing games is designed here for more natural communication with the machines. The system presented here is a vision-based system for detection of long voluntary eye blinks and interpretation of blink patterns for communication between man and machine. This system replaces the mouse with the human face as a new way to interact with the computer. Facial features (nose tip and eyes) are detected and tracked in realtime to use their actions as mouse events. The coordinates and movement of the nose tip in the live video feed are translated to become the coordinates and movement of the mouse pointer on the application. The left or right eye blinks fire left or right mouse click events. The system works with inexpensive USB cameras and runs at a frame rate of 30 frames per second.


Using Statistical Moment Invariants and Entropy in Image Retrieval

Abstract: Although content-based image retrieval (CBIR) is not a new subject, it keeps attracting more and more attention, as the amount of images grow tremendously due to internet, inexpensive hardware and automation of image acquisition. One of the applications of CBIR is fetching images from a database. This paper presents a new method for automatic image retrieval using moment invariants and image entropy, our technique could be used to find semi or perfect matches based on query by example manner, experimental results demonstrate that the purposed technique is scalable and efficient.


Genetic Algorithm Based Optimization of Clustering in Ad Hoc Networks

Abstract: In this paper, we have to concentrate on implementation of Weighted Clustering Algorithm with the help of Genetic Algorithm (GA).Here we have developed new algorithm for the implementation of GA-based approach with the help of Weighted Clustering Algorithm (WCA) (4). ClusterHead chosen is a important thing for clustering in adhoc networks. So, we have shown the optimization technique for the minimization of ClusterHeads(CH) based on some parameter such as degree difference, Battery power (Pv), degree of mobility, and sum of the distances of a node in adhoc networks. ClusterHeads selection of adhoc networks is an important thing for clustering. Here, we have discussed the performance comparison between deterministic approach and GA based approach. In this performance comparison, we have seen that GA does not always give the good result compare to deterministic WCA algorithm. Here we have seen connectivity (connectivity can be measured by the probability that a node is reachable to any other node.) is better than the deterministic WCA algorithm (4).


Multi Product Inventory Optimization using Uniform Crossover Genetic Algorithm

Abstract: Inventory management is considered to be an important field in Supply Chain Management because the cost of inventories in a supply chain accounts for about 30 percent of the value of the product. The service provided to the customer eventually gets enhanced once the efficient and effective management of inventory is carried out all through the supply chain. The precise estimation of optimal inventory is essential since shortage of inventory yields to lost sales, while excess of inventory may result in pointless storage costs. Thus the determination of the inventory to be held at various levels in a supply chain becomes inevitable so as to ensure minimal cost for the supply chain. The minimization of the total supply chain cost can only be achieved when optimization of the base stock level is carried out at each member of the supply chain. This paper deals with the problem of determination of base stock levels in a ten member serial supply chain with multiple products produced by factories using Uniform Crossover Genetic Algorithms. The complexity of the problem increases when more distribution centers and agents and multiple products were involved. These considerations leading to very complex inventory management process has been resolved in this work.


Efficient Inventory Optimization of Multi Product, Multiple Suppliers with Lead Time using PSO

Abstract: With information revolution, increased globalization and competition, supply chain has become longer and more complicated than ever before. These developments bring supply chain management to the forefront of the managements attention. Inventories are very important in a supply chain. The total investment in inventories is enormous, and the management of inventory is crucial to avoid shortages or delivery delays for the customers and serious drain on a companys financial resources. The supply chain cost increases because of the influence of lead times for supplying the stocks as well as the raw materials. Practically, the lead times will not be same through out all the periods. Maintaining abundant stocks in order to avoid the impact of high lead time increases the holding cost. Similarly, maintaining fewer stocks because of ballpark lead time may lead to shortage of stocks. This also happens in the case of lead time involved in supplying raw materials. A better optimization methodology that utilizes the Particle Swarm Optimization algorithm, one of the best optimization algorithms, is proposed to overcome the impasse in maintaining the optimal stock levels in each member of the supply chain. Taking into account the stock levels thus obtained from the proposed methodology, an appropriate stock levels to be maintained in the approaching periods that will minimize the supply chain inventory cost can be arrived at.


Test Case Generation using Mutation Operators and Fault Classification

Abstract: Software testing is the important phase of software development process. But, this phase can be easily missed by software developers because of their limited time to complete the project. Since, software developers finish their software nearer to the delivery time; they dont get enough time to test their program by creating effective test cases. . One of the major difficulties in software testing is the generation of test cases that satisfy the given adequacy criterion Moreover, creating manual test cases is a tedious work for software developers in the final rush hours. A new approach which generates test cases can help the software developers to create test cases from software specifications in early stage of software development (before coding) and as well as from program execution traces from after software development (after coding). Heuristic techniques can be applied for creating quality test cases. Mutation testing is a technique for testing software units that has great potential for improving the quality of testing, and to assure the high reliability of software. In this paper, a mutation testing based test cases generation technique has been proposed to generate test cases from program execution trace, so that the test cases can be generated after coding. The paper details about the mutation testing implementation to generate test cases. The proposed algorithm has been demonstrated for an example.


An Intelligent System For Effective Forest Fire Detection Using Spatial Data

Abstract: The explosive growth of spatial data and extensive utilization of spatial databases emphasize the necessity for the automated discovery of spatial knowledge. In modern times, spatial data mining has emerged as an area of voluminous research. Forest fires are a chief environmental concern, causing economical and ecological damage while endangering human lives across the world. The fast or early detection of forest fires is a vital element for controlling such phenomenon. The application of remote sensing is at present a significant method for forest fires monitoring, particularly in vast and remote areas. Different methods have been presented by researchers for forest fire detection. The motivation behind this research is to obtain beneficial information from images in the forest spatial data and use the same in the determination of regions at the risk of fires by utilizing Image Processing and Artificial Intelligence techniques. This paper presents an intelligent system to detect the presence of forest fires in the forest spatial data using Artificial Neural Networks. The digital images in the forest spatial data are converted from RGB to XYZ color space and then segmented by employing anisotropic diffusion to identify the fire regions. Subsequently, Radial Basis Function Neural Network is employed in the design of the intelligent system, which is trained with the color space values of the segmented fire regions. Extensive experimental assessments on publicly available spatial data illustrated the efficiency of the proposed system in effectively detecting forest fires.


Modeling of Human Criminal Behavior using Probabilistic Networks

Abstract: Currently, criminals profile (CP) is obtained from investigators or forensic psychologists interpretation, linking crime scene characteristics and an offenders behavior to his or her characteristics and psychological profile. This paper seeks an efficient and systematic discovery of nonobvious and valuable patterns between variables from a large database of solved cases via a probabilistic network (PN) modeling approach. The PN structure can be used to extract behavioral patterns and to gain insight into what factors influence these behaviors. Thus, when a new case is being investigated and the profile variables are unknown because the offender has yet to be identified, the observed crime scene variables are used to infer the unknown variables based on their connections in the structure and the corresponding numerical (probabilistic) weights. The objective is to produce a more systematic and empirical approach to profiling, and to use the resulting PN model as a decision tool.


A proof Procedure for Testing Membership in Regular Expressions

Abstract: We propose an algorithm that test membership for regular expressions and show that the algorithm is correct. This algorithm is written in the style of a sequent proof system. The advantage of this algorithm over traditional ones is that the complex conversion process from regular expressions to finite automata is not needed. As a consequence, our algorithm is simple and extends easily to various extensions to regular expressions such as timed regular expressions or regular languages with the intersection.


An innovative platform to improve the performance of exact string matching algorithms

Abstract: Exact String Matching is an essential issue in many computer science applications. Unfortunately, the performance of Exact String Matching algorithms, namely, executing time, does not address the needs of these applications. This paper proposes a general platform for improving the existing Exact String Matching algorithms executing time, called the PXSMAlg platform. The function of this platform is to parallelize the Exact String Matching algorithms using the MPI model over the Master or Slaves paradigms. The PXSMAlg platform parallelization process is done by dividing the Text into several parts and working on these parts simultaneously. This improves the executing time of the Exact String Matching algorithms. We have simulated the PXSMAlg platform in order to show its competence, through applying the Quick Search algorithm on the PXSMAlg platform. The simulation result showed significant improvement in the Quick Search executing time, and therefore extreme competence in the PXSMAlg platform.


A Logical Product Approach to Zonotope Intersection

Abstract: We define and study a new abstract domain which is a fine-grained combination of zonotopes with polyhedric domains such as the interval, octagon, linear templates or polyhedron domain. While abstract transfer functions are still rather inexpensive and accurate even for interpreting non-linear computations, we are able to also interpret tests (i.e. intersections) efficiently. This fixes a known drawback of zonotopic methods, as used for reachability analysis for hybrid sys- tems as well as for invariant generation in abstract interpretation: intersection of zonotopes are not always zonotopes, and there is not even a best zonotopic over-approximation of the intersection. We describe some examples and an im- plementation of our method in the APRON library, and discuss some further in- teresting combinations of zonotopes with non-linear or non-convex domains such as quadratic templates and maxplus polyhedra.