Search results for: Cognitive Networks

Authors: Rade M. Ciric, Nikola Lj.Rajakovic

Abstract:

Since the 80s huge efforts have been made to utilize renewable energy sources to generate electric power. This paper reports some aspects of integration of the distributed generators into the low voltage distribution networks. An assessment of impact of the distributed generators on the reliability indices of low voltage network is performed. Results obtained from case study using low voltage network, are presented and discussed.

Keywords: low voltage network, distributed generation, reliability indices

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Authors: S. Chebbi, A. Meddeb

Abstract:

The distribution networks are often exposed to harmful incidents which can halt the electricity supply of the customer. In this context, we studied a real case of a critical zone of the Tunisian network which is currently characterized by the dysfunction of its plan of protection. In this paper, we were interested in the harmonization of the protection plan settings in order to ensure a perfect selectivity and a better continuity of service on the whole of the network.

Keywords: Distribution network Gabes-Tunisia, NEPLAN©DACH, protection plan settings, selectivity.

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Authors: Sitalakshmi Venkataraman

Abstract:

Recent scientific investigations indicate that multimodal biometrics overcome the technical limitations of unimodal biometrics, making them ideally suited for everyday life applications that require a reliable authentication system. However, for a successful adoption of multimodal biometrics, such systems would require large heterogeneous datasets with complex multimodal fusion and privacy schemes spanning various distributed environments. From experimental investigations of current multimodal systems, this paper reports the various issues related to speed, error-recovery and privacy that impede the diffusion of such systems in real-life. This calls for a robust mechanism that caters to the desired real-time performance, robust fusion schemes, interoperability and adaptable privacy policies. The main objective of this paper is to present a framework that addresses the abovementioned issues by leveraging on the heterogeneous resource sharing capacities of Grid services and the efficient machine learning capabilities of artificial neural networks (ANN). Hence, this paper proposes a Grid-based neural network framework for adopting multimodal biometrics with the view of overcoming the barriers of performance, privacy and risk issues that are associated with shared heterogeneous multimodal data centres. The framework combines the concept of Grid services for reliable brokering and privacy policy management of shared biometric resources along with a momentum back propagation ANN (MBPANN) model of machine learning for efficient multimodal fusion and authentication schemes. Real-life applications would be able to adopt the proposed framework to cater to the varying business requirements and user privacies for a successful diffusion of multimodal biometrics in various day-to-day transactions.

Keywords: Back Propagation, Grid Services, MultimodalBiometrics, Neural Networks.

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Authors: Akim Borbuev, Francisco de León

Abstract:

Economic and population growth in densely-populated urban areas introduce major challenges to distribution system operators, planers, and designers. To supply added loads, utilities are frequently forced to invest in new distribution feeders. However, this is becoming increasingly more challenging due to space limitations and rising installation costs in urban settings. This paper proposes the conversion of critical alternating current (ac) distribution feeders into direct current (dc) feeders to increase the power transfer capacity by a factor as high as four. Current trends suggest that the return of dc transmission, distribution, and utilization are inevitable. Since a total system-level transformation to dc operation is not possible in a short period of time due to the needed huge investments and utility unreadiness, this paper recommends that feeders that are expected to exceed their limits in near future are converted to dc. The increase in power transfer capacity is achieved through several key differences between ac and dc power transmission systems. First, it is shown that underground cables can be operated at higher dc voltage than the ac voltage for the same dielectric stress in the insulation. Second, cable sheath losses, due to induced voltages yielding circulation currents, that can be as high as phase conductor losses under ac operation, are not present under dc. Finally, skin and proximity effects in conductors and sheaths do not exist in dc cables. The paper demonstrates that in addition to the increased power transfer capacity utilities substituting ac feeders by dc feeders could benefit from significant lower costs and reduced losses. Installing dc feeders is less expensive than installing new ac feeders even when new trenches are not needed. Case studies using the IEEE 342-Node Low Voltage Networked Test System quantify the technical and economic benefits of dc feeders.

Keywords: Dc power systems, distribution feeders, distribution networks, energy efficiency, power transfer capacity.

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Authors: Touahria Mohamed, Khababa Abdallah, Frécon Louis

Abstract:

This paper proposes a declarative language for knowledge representation (Ibn Rochd), and its environment of exploitation (DeGSE). This DeGSE system was designed and developed to facilitate Ibn Rochd writing applications. The system was tested on several knowledge bases by ascending complexity, culminating in a system for recognition of a plant or a tree, and advisors to purchase a car, for pedagogical and academic guidance, or for bank savings and credit. Finally, the limits of the language and research perspectives are stated.

Keywords: Knowledge representation, declarative language, IbnRochd, DeGSE, facets, cognitive approach.

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Authors: Semanur Soyyiğit Kaya, Ercan Eren

Abstract:

Energy has a prominent role for development of nations. Countries which have energy resources also have strategic power in the international trade of energy since it is essential for all stages of production in the economy. Thus, it is important for countries to analyze the weaknesses and strength of the system. On the other side, international trade is one of the fields that are analyzed as a complex network via network analysis. Complex network is one of the tools to analyze complex systems with heterogeneous agents and interaction between them. A complex network consists of nodes and the interactions between these nodes. Total properties which emerge as a result of these interactions are distinct from the sum of small parts (more or less) in complex systems. Thus, standard approaches to international trade are superficial to analyze these systems. Network analysis provides a new approach to analyze international trade as a network. In this network, countries constitute nodes and trade relations (export or import) constitute edges. It becomes possible to analyze international trade network in terms of high degree indicators which are specific to complex networks such as connectivity, clustering, assortativity/disassortativity, centrality, etc. In this analysis, international trade of crude oil and coal which are types of fossil fuel has been analyzed from 2005 to 2014 via network analysis. First, it has been analyzed in terms of some topological parameters such as density, transitivity, clustering etc. Afterwards, fitness to Pareto distribution has been analyzed via Kolmogorov-Smirnov test. Finally, weighted HITS algorithm has been applied to the data as a centrality measure to determine the real prominence of countries in these trade networks. Weighted HITS algorithm is a strong tool to analyze the network by ranking countries with regards to prominence of their trade partners. We have calculated both an export centrality and an import centrality by applying w-HITS algorithm to the data. As a result, impacts of the trading countries have been presented in terms of high-degree indicators.

Keywords: Complex network approach, fossil fuel, international trade, network theory.

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Authors: Mario Gonzalez, David Dominguez, Francisco B. Rodriguez

Abstract:

An attractor neural network on the small-world topology is studied. A learning pattern is presented to the network, then a stimulus carrying local information is applied to the neurons and the retrieval of block-like structure is investigated. A synaptic noise decreases the memory capability. The change of stability from local to global attractors is shown to depend on the long-range character of the network connectivity.

Keywords: Hebbian learning, image recognition, small world, spatial information.

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Authors: Ernst D. Schmitter

Abstract:

Monitoring lightning electromagnetic pulses (sferics) and other terrestrial as well as extraterrestrial transient radiation signals is of considerable interest for practical and theoretical purposes in astro- and geophysics as well as meteorology. Managing a continuous flow of data, automisation of the detection and classification process is important. Features based on a combination of wavelet and statistical methods proved efficient for analysis and characterisation of transients and as input into a radial basis function network that is trained to discriminate transients from pulse like to wave like.

Keywords: transient signals, statistics, wavelets, neural networks

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Authors: M. Rodionov, Z. Dedovets

Abstract:

This article discusses ways to implement a differentiated approach to developing academic motivation for mathematical studies which relies on defining the primary structural characteristics of motivation. The following characteristics are considered: features of realization of cognitive activity, meaningmaking characteristics, level of generalization and consistency of knowledge acquired by personal experience. The assessment of the present level of individual student understanding of each component of academic motivation is the basis for defining the relevant educational strategy for its further development.

Keywords: Learning activity, mathematics, motivation, student.

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Authors: Lilac Al Safadi, Rana Abu Nafesa, Regina Garcia

Abstract:

Motivated by Microsoft Co. Academic Program initiative, the department of Information Technology in King Saud University has adopted Microsoft products in three courses. The initiative aimed at enhancing the abilities of the university graduates and equipping them with skills that would help them in the job market. A number of methods of collecting assessment data were used to evaluate the course adoption initiative. Assessment data indicated that the goal of the course adoption is being achieved and that the students were much better prepared to design applications and administrate networks.

Keywords: course adoption, assessment, programming, technologies

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Authors: Erica Lima

Abstract:

The objective of this paper is to discuss relevant points about teaching translation in Brazilian universities and the possible impacts of blogs and social networks to translator education today. It is intended to analyze the curricula of Brazilian translation courses, contrasting them to information obtained from two social networking groups of great visibility in the area concerning essential characteristics to become a successful profession. Therefore, research has, as its main corpus, a few undergraduate translation programs’ syllabuses, as well as a few postings on social networks groups that specifically share professional opinions regarding the necessity for a translator to obtain a degree in translation to practice the profession. To a certain extent, such comments and their corresponding responses lead to the propagation of discourses which influence the ideas that aspiring translators and recent graduates end up having towards themselves and their undergraduate courses. The postings also show that many professionals do not have a clear position regarding the translator education; while refuting it, they also encourage “free” courses. It is thus observed that cyberspace constitutes, on the one hand, a place of mobilization of people in defense of similar ideas. However, on the other hand, it embodies a place of tension and conflict, in view of the fact that there are many participants and, as in any other situation of interlocution, disagreements may arise. From the postings, aspects related to professionalism were analyzed (including discussions about regulation), as well as questions about the classic dichotomies: theory/practice; art/technique; self-education/academic training. As partial result, the common interest regarding the valorization of the profession could be mentioned, although there is no consensus on the essential characteristics to be a good translator. It was also possible to observe that the set of socially constructed representations in the group reflects characteristics of the world situation of the translation courses (especially in some European countries and in the United States), which, in the first instance, does not accurately reflect the Brazilian idiosyncrasies of the area.

Keywords: Cyberspace, teaching translation, translator education, university.

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Authors: R. Poonkuzhali, M. Y. Sanavullah, M. R. Gurupriya

Abstract:

Opportunistic routing is used, where the network has the features like dynamic topology changes and intermittent network connectivity. In Delay tolerant network or Disruption tolerant network opportunistic forwarding technique is widely used. The key idea of opportunistic routing is selecting forwarding nodes to forward data packets and coordination among these nodes to avoid duplicate transmissions. This paper gives the analysis of pros and cons of various opportunistic routing techniques used in MANET.

Keywords: Expected Transmission Count (ETX), Opportunistic routing, Proactive Source Routing (PSR), throughput.

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Authors: Houman Tamaddon, Mehrdad Behnia, Masud Behnia

Abstract:

An accurate study of blood flow is associated with an accurate vascular pattern and geometrical properties of the organ of interest. Due to the complexity of vascular networks and poor accessibility in vivo, it is challenging to reconstruct the entire vasculature of any organ experimentally. The objective of this study is to introduce an innovative approach for the reconstruction of a full vascular tree from available morphometric data. Our method consists of implementing morphometric data on those parts of the vascular tree that are smaller than the resolution of medical imaging methods. This technique reconstructs the entire arterial tree down to the capillaries. Vessels greater than 2 mm are obtained from direct volume and surface analysis using contrast enhanced computed tomography (CT). Vessels smaller than 2mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray’s Laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray’s Laws to every vessel bifurcation simultaneously, lead to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. During the simulation, we used the averaged flow rate for each order to predict the pressure drop and once the pressure drop is predicted, the flow rate is corrected to match the computed pressure drop for each vessel. The final results for 3 cardiac cycles is presented and compared to the clinical data.

Keywords: Blood flow, Morphometric data, Vascular tree, Strahler ordering system.

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Authors: Toshihiko Matsuka

Abstract:

One problem in evaluating recent computational models of human category learning is that there is no standardized method for systematically comparing the models' assumptions or hypotheses. In the present study, a flexible general model (called GECLE) is introduced that can be used as a framework to systematically manipulate and compare the effects and descriptive validities of a limited number of assumptions at a time. Two example simulation studies are presented to show how the GECLE framework can be useful in the field of human high-order cognition research.

Keywords: artificial intelligence, category learning, cognitive modeling, radial basis functions.

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Authors: Katie Emerson, Sara Perez-Ojalvo, Jim Komorowski, Danielle Greenberg

Abstract:

The purpose of this study was to evaluate arginase activity levels in response to combinations of an inositol-stabilized arginine silicate (ASI; Nitrosigine®), L-arginine, and Inositol. Arginine acts as a vasodilator that promotes increased blood flow resulting in enhanced delivery of oxygen and nutrients to the brain and other tissues. Arginase, found in human serum, catalyzes the conversion of arginine to ornithine and urea, completing the last step in the urea cycle. Decreasing arginase levels maintains arginine and results in increased nitric oxide production. This study aimed to determine the most effective combination of ASI, L-arginine and inositol for minimizing arginase levels and therefore maximize ASI’s effect on cognition. Serum was taken from untreated healthy donors by separation from clotted factors. Arginase activity of serum in the presence or absence of test products was determined (QuantiChrom™, DARG-100, Bioassay Systems, Hayward CA). The remaining ultra-filtrated serum units were harvested and used as the source for the arginase enzyme. ASI alone or combined with varied levels of Inositol were tested as follows: ASI + inositol at 0.25 g, 0.5 g, 0.75 g, or 1.00 g. L-arginine was also tested as a positive control. All tests elicited changes in arginase activity demonstrating the efficacy of the method used. Adding L-arginine to serum from untreated subjects, with or without inositol only had a mild effect. Adding inositol at all levels reduced arginase activity. Adding 0.5 g to the standardized amount of ASI led to the lowest amount of arginase activity as compared to the 0.25 g, 0.75 g or 1.00g doses of inositol or to L-arginine alone. The outcome of this study demonstrates an interaction of the pairing of inositol with ASI on the activity of the enzyme arginase. We found that neither the maximum nor minimum amount of inositol tested in this study led to maximal arginase inhibition. Since the inhibition of arginase activity is desirable for product formulations looking to maintain arginine levels, the most effective amount of inositol was deemed preferred. Subsequent studies suggest this moderate level of inositol in combination with ASI leads to cognitive improvements including reaction time, executive function, and concentration.

Keywords: Arginine, blood flow, colorimetry, urea cycle.

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Authors: Amir Abolfazl Suratgar, Mohammad Bagher Tavakoli, Abbas Hoseinabadi

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In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method.

Keywords: Levenberg-Marquardt, modification, neural network, variable learning rate.

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Authors: Babiga Birregah, Prosper K. Doh, Kondo H. Adjallah

Abstract:

In this work we present some matrix operators named circulant operators and their action on square matrices. This study on square matrices provides new insights into the structure of the space of square matrices. Moreover it can be useful in various fields as in agents networking on Grid or large-scale distributed self-organizing grid systems.

Keywords: Pascal matrices, Binomial Recursion, Circulant Operators, Square Matrix Bipartition, Local Network, Parallel networks of agents.

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Authors: A. M. Pashayev, D. D. Askerov, C. Ardil, R. A. Sadiqov, P. S. Abdullayev

Abstract:

Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.

Keywords: aviation gas turbine engine, technical condition, fuzzy logic, neural networks, fuzzy statistics

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Authors: Erik Pontiskoski, Kazuhiro Asakawa

Abstract:

This is a conceptual paper on the application of open innovation in three case examples of Apple, Nintendo, and Nokia. Utilizing key concepts from research into managerial and organizational cognition, we describe how each company overcame barriers to utilizing open innovation strategy in R&D and commercialization projects. We identify three levels of barriers: cognitive, behavioral, and institutional, and describe the companies balanced between internal and external resources to launch products that were instrumental in companies reinventing themselves in mature markets.

Keywords: managerial cognition, open innovation.

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Authors: Ivan Traina, Luigi Sangalli, Fabio Tognon, Angelo Lascioli

Abstract:

Currently, challenges regarding preschooler children are mainly focused on a sedentary lifestyle. Also, motor activity in infancy is seen as a tool for the separate acquisition of cognitive and socio-emotional skills rather than considering neuromotor development as a tool for improving learning abilities. The paper utilized an observational research method to shed light on the results of practicing neuromotor exercises in preschool children with disability as well as provide implications for practice.

Keywords: Children with disability, learning abilities, inclusion, neuromotor development.

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Authors: Chia-Jung Tsai, Shin-Shin Kao

Abstract:

The hypercube Qn is one of the most well-known and popular interconnection networks and the k-ary n-cube Qk n is an enlarged family from Qn that keeps many pleasing properties from hypercubes. In this article, we study the panpositionable hamiltonicity of Qk n for k ≥ 3 and n ≥ 2. Let x, y of V (Qk n) be two arbitrary vertices and C be a hamiltonian cycle of Qk n. We use dC(x, y) to denote the distance between x and y on the hamiltonian cycle C. Define l as an integer satisfying d(x, y) ≤ l ≤ 1 2 |V (Qk n)|. We prove the followings: • When k = 3 and n ≥ 2, there exists a hamiltonian cycle C of Qk n such that dC(x, y) = l. • When k ≥ 5 is odd and n ≥ 2, we request that l /∈ S where S is a set of specific integers. Then there exists a hamiltonian cycle C of Qk n such that dC(x, y) = l. • When k ≥ 4 is even and n ≥ 2, we request l-d(x, y) to be even. Then there exists a hamiltonian cycle C of Qk n such that dC(x, y) = l. The result is optimal since the restrictions on l is due to the structure of Qk n by definition.

Keywords: Hamiltonian, panpositionable, bipanpositionable, k-ary n-cube.

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

Abstract:

Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: Artificial Neural Network, Decision Support System, drug abuse, drug addiction, Multilayer Perceptron.

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Authors: Hasanul Ferdaus, Sazzadur Rahman, Kamrul Islam

Abstract:

With the rapid usage of portable devices mobility in IP networks becomes more important issue in the recent years. IETF standardized Mobile IP that works in Network Layer, which involves tunneling of IP packets from HA to Foreign Agent. Mobile IP suffers many problems of Triangular Routing, conflict with private addressing scheme, increase in load in HA, need of permanent home IP address, tunneling itself, and so on. In this paper, we proposed mobility management in Application Layer protocol SIP and show some comparative analysis between Mobile IP and SIP in context of mobility.

Keywords: Mobility, mobile IP, SIP, tunneling.

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Authors: S. Pradittara, M. Uthansakul, P. Uthansakul

Abstract:

A concept of switched beam antennas consisting of 2×2 rectangular array spaced by λ/4 accompanied with a null locating has been proposed in the previous work. In this letter, the performance evaluations of its prototype are presented. The benefits of using proposed system have been clearly measured in term of signal quality, throughput and delays. Also, the impact of position shift which mesh router is not located on the expected beam direction has also been investigated.

Keywords: Antenna array, Beamforming, Null steering, WMNs.

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Authors: Padmanabhan Balasubramanian, C. Hari Narayanan

Abstract:

This paper discusses a new, systematic approach to the synthesis of a NP-hard class of non-regenerative Boolean networks, described by FON[FOFF]={mi}[{Mi}], where for every mj[Mj]∈{mi}[{Mi}], there exists another mk[Mk]∈{mi}[{Mi}], such that their Hamming distance HD(mj, mk)=HD(Mj, Mk)=O(n), (where 'n' represents the number of distinct primary inputs). The method automatically ensures exact minimization for certain important selfdual functions with 2n-1 points in its one-set. The elements meant for grouping are determined from a newly proposed weighted incidence matrix. Then the binary value corresponding to the candidate pair is correlated with the proposed binary value matrix to enable direct synthesis. We recommend algebraic factorization operations as a post processing step to enable reduction in literal count. The algorithm can be implemented in any high level language and achieves best cost optimization for the problem dealt with, irrespective of the number of inputs. For other cases, the method is iterated to subsequently reduce it to a problem of O(n-1), O(n-2),.... and then solved. In addition, it leads to optimal results for problems exhibiting higher degree of adjacency, with a different interpretation of the heuristic, and the results are comparable with other methods. In terms of literal cost, at the technology independent stage, the circuits synthesized using our algorithm enabled net savings over AOI (AND-OR-Invert) logic, AND-EXOR logic (EXOR Sum-of- Products or ESOP forms) and AND-OR-EXOR logic by 45.57%, 41.78% and 41.78% respectively for the various problems. Circuit level simulations were performed for a wide variety of case studies at 3.3V and 2.5V supply to validate the performance of the proposed method and the quality of the resulting synthesized circuits at two different voltage corners. Power estimation was carried out for a 0.35micron TSMC CMOS process technology. In comparison with AOI logic, the proposed method enabled mean savings in power by 42.46%. With respect to AND-EXOR logic, the proposed method yielded power savings to the tune of 31.88%, while in comparison with AND-OR-EXOR level networks; average power savings of 33.23% was obtained.

Keywords: AOI logic, ESOP, AND-OR-EXOR, Incidencematrix, Hamming distance.

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Authors: Rongtao Ding, Xinhao Ji, Linting Zhu

Abstract:

In recent years, the relevance feedback technology is regarded in content-based image retrieval. This paper suggests a neural networks feedback algorithm based on the radial basis function, coming to extract the semantic character of image. The results of experiment indicated that the performance of this relevance feedback is better than the feedback algorithm based on Single-RBF.

Keywords: Image retrieval, relevance feedback, radial basis function.

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Authors: H. El-Madbouly

Abstract:

In this paper, genetic algorithm (GA) is proposed for the design of an optimization algorithm to achieve the bandwidth allocation of ATM network. In Broadband ISDN, the ATM is a highbandwidth; fast packet switching and multiplexing technique. Using ATM it can be flexibly reconfigure the network and reassign the bandwidth to meet the requirements of all types of services. By dynamically routing the traffic and adjusting the bandwidth assignment, the average packet delay of the whole network can be reduced to a minimum. M/M/1 model can be used to analyze the performance.

Keywords: Bandwidth allocation, Genetic algorithm, ATMNetwork, packet delay.

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Authors: Muhammad Raza Ali, Tim Morris

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Human computer interaction has progressed considerably from the traditional modes of interaction. Vision based interfaces are a revolutionary technology, allowing interaction through human actions, gestures. Researchers have developed numerous accurate techniques, however, with an exception to few these techniques are not evaluated using standard HCI techniques. In this paper we present a comprehensive framework to address this issue. Our evaluation of a computer vision application shows that in addition to the accuracy, it is vital to address human factors

Keywords: Usability evaluation, cognitive walkthrough, think aloud, gesture recognition.

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Authors: A. Denker, U. Akıncıoğlu

Abstract:

In this paper a neural adaptive control method has been developed and applied to robot control. Simulation results are presented to verify the effectiveness of the controller. These results show that the performance by using this controller is better than those which just use either direct inverse control or predictive control. In addition, they show that the resulting is a useful method which combines the advantages of both direct inverse control and predictive control.

Keywords: Neural networks, robotics, direct inverse control, predictive control.

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Authors: Eduardo Sant'Ana da Silva, Andre Luiz Pires Guedes, Eduardo Todt

Abstract:

Independent spanning trees (ISTs) provide a number of advantages in data broadcasting. One can cite the use in fault tolerance network protocols for distributed computing and bandwidth. However, the problem of constructing multiple ISTs is considered hard for arbitrary graphs. In this paper we present an efficient algorithm to construct ISTs on hypercubes that requires minimum resources to be performed.

Keywords: Hypercube, Independent Spanning Trees, Networks On Chip, Systems On Chip.

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