Search results for: distributed fusion architecture

Authors: Bassma Aldahlan, Zongming Fei

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Recently, there is a great interest in smart parking application. Theses applications are enhanced by a vehicular ad-hoc network, which helps drivers find and reserve satiable packing spaces for a period of time ahead of time. Named Data Networking (NDN) is a future Internet architecture that benefits vehicular ad-hoc networks because of its clean-slate design and pure communication model. In this paper, we proposed an NDN-based frame-work for smart parking that involved a fog computing architecture. The proposed application had two main directions: First, we allowed drivers to query the number of parking spaces in a particular parking lot. Second, we introduced a technique that enabled drivers to make intelligent reservations before their arrival time. We also introduced a “push-based” model supporting the NDN-based framework for smart parking applications. To evaluate the proposed solution’s performance, we analyzed the function for finding parking lots with available parking spaces and the function for reserving a parking space. Our system showed high performance results in terms of response time and push overhead. The proposed reservation application performed better than the baseline approach.

Keywords: Cloud Computing, Vehicular Named Data Networking, Smart Parking Applications, Fog Computing.

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Authors: Fatima Akhmedova, Simon Liao

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One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, nonredundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: Face Recognition, Hahn moments, Recognition-by-parts, Time-lapse.

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Authors: Abdelhamid Louliej, Younes Jabrane

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Satellites are becoming indispensable assets to fifth-generation (5G) new radio architecture, complementing wireless and terrestrial communication links. The combination of satellites and 5G architecture allows consumers to access all next-generation services anytime, anywhere, including scenarios, like traveling to remote areas (without coverage). Nevertheless, this solution faces several challenges, such as a significant propagation delay, Doppler frequency shift, and high Peak-to-Average Power Ratio (PAPR), causing signal distortion due to the non-linear saturation of the High-Power Amplifier (HPA). To compensate for HPA non-linearity in 5G satellite transmission, an efficient pre-distorter scheme using Neural Networks (NN) is proposed. To assess the proposed NN pre-distorter, two types of HPA were investigated: Travelling Wave Tube Amplifier (TWTA) and Solid-State Power Amplifier (SSPA). The results show that the NN pre-distorter design presents an Error Vector Magnitude (EVM) improvement by 95.26%. Normalized Mean Square Error (NMSE) and Adjacent Channel Power Ratio (ACPR) were reduced by -43,66 dB and 24.56 dBm, respectively. Moreover, the system suffers no degradation of the Bit Error Rate (BER) for TWTA and SSPA amplifiers.

Keywords: Satellites, 5G, Neural Networks, High-Power Amplifier, Travelling Wave Tube Amplifier, Solid-State Power Amplifier, EVM, NMSE, ACPR.

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Authors: Jihad S. Daba, J. P. Dubois

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Fading noise degrades the performance of cellular communication, most notably in femto- and pico-cells in 3G and 4G systems. When the wireless channel consists of a small number of scattering paths, the statistics of fading noise is not analytically tractable and poses a serious challenge to developing closed canonical forms that can be analysed and used in the design of efficient and optimal receivers. In this context, noise is multiplicative and is referred to as stochastically local fading. In many analytical investigation of multiplicative noise, the exponential or Gamma statistics are invoked. More recent advances by the author of this paper utilized a Poisson modulated-weighted generalized Laguerre polynomials with controlling parameters and uncorrelated noise assumptions. In this paper, we investigate the statistics of multidiversity stochastically local area fading channel when the channel consists of randomly distributed Rayleigh and Rician scattering centers with a coherent Nakagami-distributed line of sight component and an underlying doubly stochastic Poisson process driven by a lognormal intensity. These combined statistics form a unifying triply stochastic filtered marked Poisson point process model.

Keywords: Cellular communication, femto- and pico-cells, stochastically local area fading channel, triply stochastic filtered marked Poisson point process.

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Authors: Emmanuel D. Buedi, K. O. Boateng, Griffith S. Klogo

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Electrical distribution networks are prone to power quality disturbances originating from the complexity of the distribution network, mode of distribution (overhead or underground) and types of loads used by customers. Data on the types of disturbances present and frequency of occurrence is needed for economic evaluation and hence finding solution to the problem. Utility companies have resorted to using secondary power quality devices such as smart meters to help gather the required data. Even though this approach is easier to adopt, data gathered from these devices may not serve the required purpose, since the installation of these devices in the electrical network usually does not conform to available PQM placement methods. This paper presents a design of a PQM that is capable of integrating into an existing DAS infrastructure to take advantage of available placement methodologies. The monitoring component of the design is implemented and installed to monitor an existing LV network. Data from the monitor is analyzed and presented. A portion of the LV network of the Electricity Company of Ghana is modeled in MATLAB-Simulink and analyzed under various earth fault conditions. The results presented show the ability of the PQM to detect and analyze PQ disturbance such as voltage sag and overvoltage. By adopting a placement methodology and installing these nodes, utilities are assured of accurate and reliable information with respect to the quality of power delivered to consumers.

Keywords: Power quality, remote monitoring, distributed automation system, economic evaluation, LV network.

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Authors: Ning Gong, Michael Korostelev, Qiangguo Ren, Li Bai, Saroj Biswas, Frank Ferrese

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This paper investigates the joint effect of the interconnected (n,k)-star network topology and Multi-Agent automated control on restoration and reconfiguration of power systems. With the increasing trend in development in Multi-Agent control technologies applied to power system reconfiguration in presence of faulty components or nodes. Fault tolerance is becoming an important challenge in the design processes of the distributed power system topology. Since the reconfiguration of a power system is performed by agent communication, the (n,k)-star interconnected network topology is studied and modeled in this paper to optimize the process of power reconfiguration. In this paper, we discuss the recently proposed (n,k)-star topology and examine its properties and advantages as compared to the traditional multi-bus power topologies. We design and simulate the topology model for distributed power system test cases. A related lemma based on the fault tolerance and conditional diagnosability properties is presented and proved both theoretically and practically. The conclusion is reached that (n,k)-star topology model has measurable advantages compared to standard bus power systems while exhibiting fault tolerance properties in power restoration, as well as showing efficiency when applied to power system route discovery.

Keywords: (n, k)-star Topology, Fault Tolerance, Conditional Diagnosability, Multi-Agent System, Automated Power System.

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Authors: Richard E. Miller

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12.7-mm thick plates of 6061-T6511 aluminum alloy and high hardness steel (528 HV) were successfully joined by a friction stir bonding process using a tungsten-rhenium stir tool. Process parameter variation experiments, which included tool design geometry, plunge and traverse rates, tool offset, spindle tilt, and rotation speed, were conducted to develop a parameter set which yielded a defect free joint. Laboratory tensile tests exhibited yield stresses which exceed the strengths of comparable AA6061-to-AA6061 fusion and friction stir weld joints. Scanning electron microscopy and energy dispersive X-ray spectroscopy analysis also show atomic diffusion at the material interface region.

Keywords: Dissimilar materials, friction stir, welding.

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Authors: R. Chedid, R. Ghajar

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Lebanon electricity crisis continues to escalate. Rationing hours still apply across the country but with different rates. The capital Beirut is subjected to 3 hours cut while other cities, town and villages may endure 9 to 14 hours of power shortage. To mitigate this situation, private diesel generators distributed illegally all over the country are being used to bridge the gap in power supply. Almost each building in large cities has its own generator and individual villages may have more than one generator supplying their loads. These generators together with their private networks form incomplete and ill-designed and managed microgrids (MG) but can be further developed to become renewable energy-based MG operating in island- or grid-connected modes. This paper will analyze the potential of introducing MG to help resolve the energy crisis in Lebanon. It will investigate the usefulness of developing MG under the prevailing situation of existing private power supply service providers and in light of the developed national energy policy that supports renewable energy development. A case study on a distribution feeder in a rural area will be analyzed using HOMER software to demonstrate the usefulness of introducing photovoltaic (PV) arrays along the existing diesel generators for all the stakeholders; namely, the developers, the customers, the utility and the community at large. Policy recommendations regarding MG development in Lebanon will be presented on the basis of the accumulated experience in private generation and the privatization and public-private partnership laws.

Keywords: Decentralized systems, microgrids, distributed generation, renewable energy.

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Authors: Amir Alani, Mojtaba Mahmoodian, Anna Romanova, Asaad Faramarzi

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Pipelines are extensively used engineering structures which convey fluid from one place to another. Most of the time, pipelines are placed underground and are encumbered by soil weight and traffic loads. Corrosion of pipe material is the most common form of pipeline deterioration and should be considered in both the strength and serviceability analysis of pipes. The study in this research focuses on concrete pipes in sewage systems (concrete sewers). This research firstly investigates how to involve the effect of corrosion as a time dependent process of deterioration in the structural and failure analysis of this type of pipe. Then three probabilistic time dependent reliability analysis methods including the first passage probability theory, the gamma distributed degradation model and the Monte Carlo simulation technique are discussed and developed. Sensitivity analysis indexes which can be used to identify the most important parameters that affect pipe failure are also discussed. The reliability analysis methods developed in this paper contribute as rational tools for decision makers with regard to the strengthening and rehabilitation of existing pipelines. The results can be used to obtain a cost-effective strategy for the management of the sewer system.

Keywords: Reliability analysis, service life prediction, Monte Carlo simulation method, first passage probability theory, gamma distributed degradation model.

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Authors: Laurent Thiry, Michel Hassenforder

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This paper presents a general approach to implement efficient queries’ interpreters in a functional programming language. Indeed, most of the standard tools actually available use an imperative and/or object-oriented language for the implementation (e.g. Java for Jena-Fuseki) but other paradigms are possible with, maybe, better performances. To proceed, the paper first explains how to model data structures and queries in a functional point of view. Then, it proposes a general methodology to get performances (i.e. number of computation steps to answer a query) then it explains how to integrate some optimization techniques (short-cut fusion and, more important, data transformations). It then compares the functional server proposed to a standard tool (Fuseki) demonstrating that the first one can be twice to ten times faster to answer queries.

Keywords: Data transformation, functional programming, information server, optimization.

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Authors: H. Gholami, M. Jalali Azizpour

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Stress Corrosion Crack (SCC) in pipeline is a type of environmentally assisted cracking (EAC), since its discovery in 1965 as a possible cause of failure in pipeline, SCC has caused, on average, one of two failures per year in the U.S, According to the NACE SCC DA a pipe line segment is considered susceptible to SCC if all of the following factors are met: The operating stress exceeds 60% of specified minimum yield strength (SMYS), the operating temperature exceeds 38°C, the segment is less than 32 km downstream from a compressor station, the age of the pipeline is greater than 10 years and the coating type is other than Fusion Bonded Epoxy(FBE). In this paper as a practical experience in NISOC, Direct Assessment (DA) Method is used for identification SCC defect in unpiggable pipeline located downstream of compressor station.

Keywords: Stress Corrosion Crack, Direct Assessment, Disbondment, Transgranular SCC, Compressor Station.

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Authors: Peng Shen Ong, Yoong Choon Chang, Chee Pun Ooi, Ettikan K. Karuppiah, Shahirina Mohd Tahir

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Falling has been one of the major concerns and threats to the independence of the elderly in their daily lives. With the worldwide significant growth of the aging population, it is essential to have a promising solution of fall detection which is able to operate at high accuracy in real-time and supports large scale implementation using multiple cameras. Field Programmable Gate Array (FPGA) is a highly promising tool to be used as a hardware accelerator in many emerging embedded vision based system. Thus, it is the main objective of this paper to present an FPGA-based solution of visual based fall detection to meet stringent real-time requirements with high accuracy. The hardware architecture of visual based fall detection which utilizes the pixel locality to reduce memory accesses is proposed. By exploiting the parallel and pipeline architecture of FPGA, our hardware implementation of visual based fall detection using FGPA is able to achieve a performance of 60fps for a series of video analytical functions at VGA resolutions (640x480). The results of this work show that FPGA has great potentials and impacts in enabling large scale vision system in the future healthcare industry due to its flexibility and scalability.

Keywords: Fall detection, FPGA, hardware implementation.

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Authors: Teyana S. Sapula, Damian D. Haule

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In online context, the design and implementation of effective remote laboratories environment is highly challenging on account of hardware and software needs. This paper presents the remote laboratory software framework modified from ilab shared architecture (ISA). The ISA is a framework which enables students to remotely acccess and control experimental hardware using internet infrastructure. The need for remote laboratories came after experiencing problems imposed by traditional laboratories. Among them are: the high cost of laboratory equipment, scarcity of space, scarcity of technical personnel along with the restricted university budget creates a significant bottleneck on building required laboratory experiments. The solution to these problems is to build web-accessible laboratories. Remote laboratories allow students and educators to interact with real laboratory equipment located anywhere in the world at anytime. Recently, many universities and other educational institutions especially in third world countries rely on simulations because they do not afford the experimental equipment they require to their students. Remote laboratories enable users to get real data from real-time hand-on experiments. To implement many remote laboratories, the system architecture should be flexible, understandable and easy to implement, so that different laboratories with different hardware can be deployed easily. The modifications were made to enable developers to add more equipment in ISA framework and to attract the new developers to develop many online laboratories.

Keywords: Batched, ISA, labserver, servicebroker.

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Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil

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In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.

Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.

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Authors: D.M. Weeraddana, K.S. Walgama, E.C. Kulasekere

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A framework to estimate the state of dynamically varying environment where data are generated from heterogeneous sources possessing partial knowledge about the environment is presented. This is entirely derived within Dempster-Shafer and Evidence Filtering frameworks. The belief about the current state is expressed as belief and plausibility functions. An addition to Single Input Single Output Evidence Filter, Multiple Input Single Output Evidence Filtering approach is introduced. Variety of applications such as situational estimation of an emergency environment can be developed within the framework successfully. Fire propagation scenario is used to justify the proposed framework, simulation results are presented.

Keywords: Dempster-Shafer Belief theory, Evidence Filtering, Evidence Fusion, Sensor Modalities, Wireless Sensor Networks

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Authors: Yacine Challal, Hamida Seba

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Group key management is an important functional building block for any secure multicast architecture. Thereby, it has been extensively studied in the literature. In this paper we present relevant group key management protocols. Then, we compare them against some pertinent performance criteria.

Keywords: Multicast, Security, Group Key Management.

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Authors: K. Kalaiselvan, A. Elango

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Laser beam welding of dissimilar sheet metal combinations such as Ti/Al, SS/Al and Cu/Al are increasingly demanded due to high energy densities with less fusion and heat affected zones. A good weld joint strength involves combinations of dissimilar metals and the formation of solid solution in the weld pool. Many metal pairs suffer from significant intermetallic phase formation during welding which greatly reduces their strength. The three different sheet metal mentioned above is critically reviewed and phase diagram for the combinations are given. The aim of this study is to develop an efficient metal combinations and the influence on their interfacial characteristics. For that the following parameters such as weld geometry, residual distortion, micro hardness, microstructure and mechanical properties are analyzed systematically.

Keywords: Laser Beam Welding (LBW), dissimilar metals, Ti/Al, SS/Al and Cu/Al sheets.

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Authors: K. Kalaiselvan, A. Elango, N. M. Nagarajan

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Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.

Keywords: Laser Beam Welding (LBW), Artificial Neural Networks (ANN), Optimization, Titanium and Aluminium sheets.

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Authors: Edikan E. Akpanibah Obinichi C. Mandah Imoleayo S. Asiwaju

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In this paper, we studied the effect of supplementary premium on the optimal portfolio policy in a defined contribution (DC) pension scheme with refund of premium clauses. This refund clause allows death members’ next of kin to withdraw their relative’s accumulated wealth during the accumulation period. The supplementary premium is to help sustain the scheme and is assumed to be stochastic. We considered cases when the remaining wealth is equally distributed and when it is not equally distributed among the remaining members. Next, we considered investments in cash and equity to help increase the remaining accumulated funds to meet up with the retirement needs of the remaining members and composed the problem as a continuous time mean-variance stochastic optimal control problem using the actuarial symbol and established an optimization problem from the extended Hamilton Jacobi Bellman equations. The optimal portfolio policy, the corresponding optimal fund size for the two assets and also the efficient frontier of the pension members for the two cases was obtained. Furthermore, the numerical simulations of the optimal portfolio policies with time were presented and the effect of the supplementary premium on the optimal portfolio policy was discussed and observed that the supplementary premium decreases the optimal portfolio policy of the risky asset (equity). Secondly we observed a disparity between the optimal policies for the two cases.

Keywords: Defined contribution pension scheme, extended Hamilton Jacobi Bellman equations, optimal portfolio policies, refund of premium clauses, supplementary premium.

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Authors: Yacine Challal, Abdelmadjid Bouabdallah, Hamida Seba

Abstract:

Group key management is an important functional building block for any secure multicast architecture. Thereby, it has been extensively studied in the literature. In this paper we present relevant group key management protocols. Then, we compare them against some pertinent performance criteria.

Keywords: Multicast, Security, Group Key Management.

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Authors: Yee Ling Boo, Damminda Alahakoon

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Biologically human brain processes information in both unimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model is demonstrated and discussed with some experimental results.

Keywords: Multimodal, Granularity, Hierarchical Clustering, Growing Self Organising Maps, Data Mining

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Authors: Pucheng Zhou, Bingrong Hong

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How to coordinate the behaviors of the agents through learning is a challenging problem within multi-agent domains. Because of its complexity, recent work has focused on how coordinated strategies can be learned. Here we are interested in using reinforcement learning techniques to learn the coordinated actions of a group of agents, without requiring explicit communication among them. However, traditional reinforcement learning methods are based on the assumption that the environment can be modeled as Markov Decision Process, which usually cannot be satisfied when multiple agents coexist in the same environment. Moreover, to effectively coordinate each agent-s behavior so as to achieve the goal, it-s necessary to augment the state of each agent with the information about other existing agents. Whereas, as the number of agents in a multiagent environment increases, the state space of each agent grows exponentially, which will cause the combinational explosion problem. Profit sharing is one of the reinforcement learning methods that allow agents to learn effective behaviors from their experiences even within non-Markovian environments. In this paper, to remedy the drawback of the original profit sharing approach that needs much memory to store each state-action pair during the learning process, we firstly address a kind of on-line rational profit sharing algorithm. Then, we integrate the advantages of modular learning architecture with on-line rational profit sharing algorithm, and propose a new modular reinforcement learning model. The effectiveness of the technique is demonstrated using the pursuit problem.

Keywords: Multi-agent learning; reinforcement learning; rationalprofit sharing; modular architecture.

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Authors: Yu-Cheng Chou, Po Ting Lin

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The network of delivering commodities has been an important design problem in our daily lives and many transportation applications. The delivery performance is evaluated based on the system reliability of delivering commodities from a source node to a sink node in the network. The system reliability is thus maximized to find the optimal routing. However, the design problem is not simple because (1) each path segment has randomly distributed attributes; (2) there are multiple commodities that consume various path capacities; (3) the optimal routing must successfully complete the delivery process within the allowable time constraints. In this paper, we want to focus on the design optimization of the Multi-State Flow Network (MSFN) for multiple commodities. We propose an efficient approach to evaluate the system reliability in the MSFN with respect to randomly distributed path attributes and find the optimal routing subject to the allowable time constraints. The delivery rates, also known as delivery currents, of the path segments are evaluated and the minimal-current arcs are eliminated to reduce the complexity of the MSFN. Accordingly, the correct optimal routing is found and the worst-case reliability is evaluated. It has been shown that the reliability of the optimal routing is at least higher than worst-case measure. Two benchmark examples are utilized to demonstrate the proposed method. The comparisons between the original and the reduced networks show that the proposed method is very efficient.

Keywords: Multiple Commodities, Multi-State Flow Network (MSFN), Time Constraints, Worst-Case Reliability (WCR)

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Authors: Hossein Mirzaee, Farhad Besharati

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Solar sunspot rotation, latitudinal bands are studied based on intelligent computation methods. A combination of image fusion method with together tree decomposition is used to obtain quantitative values about the latitudes of trajectories on sun surface that sunspots rotate around them. Daily solar images taken with SOlar and Heliospheric (SOHO) satellite are fused for each month separately .The result of fused image is decomposed with Quad Tree decomposition method in order to achieve the precise information about latitudes of sunspot trajectories. Such analysis is useful for gathering information about the regions on sun surface and coordinates in space that is more expose to solar geomagnetic storms, tremendous flares and hot plasma gases permeate interplanetary space and help human to serve their technical systems. Here sunspot images in September, November and October in 2001 are used for studying the magnetic behavior of sun.

Keywords: Quad tree decomposition, sunspot image.

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Authors: A. Greco, N. Mammone, F.C. Morabito, M.Versaci

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This paper is mainly concerned with the application of a novel technique of data interpretation for classifying measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artificial Neural Networks and Multi-Class Support Vector Machines have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compared with earlier methods.

Keywords: Tokamak, Classification, Artificial Neural Network, Support Vector Machines.

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Authors: Omar F. Hamad, T. Marwala

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With optimized bandwidth and latency discrepancy ratios, Node Gain Scores (NGSs) are determined and used as a basis for shaping the max-heap overlay. The NGSs - determined as the respective bandwidth-latency-products - govern the construction of max-heap-form overlays. Each NGS is earned as a synergy of discrepancy ratio of the bandwidth requested with respect to the estimated available bandwidth, and latency discrepancy ratio between the nodes and the source node. The tree leads to enhanceddelivery overlay multicasting – increasing packet delivery which could, otherwise, be hindered by induced packet loss occurring in other schemes not considering the synergy of these parameters on placing the nodes on the overlays. The NGS is a function of four main parameters – estimated available bandwidth, Ba; individual node's requested bandwidth, Br; proposed node latency to its prospective parent (Lp); and suggested best latency as advised by source node (Lb). Bandwidth discrepancy ratio (BDR) and latency discrepancy ratio (LDR) carry weights of α and (1,000 - α ) , respectively, with arbitrary chosen α ranging between 0 and 1,000 to ensure that the NGS values, used as node IDs, maintain a good possibility of uniqueness and balance between the most critical factor between the BDR and the LDR. A max-heap-form tree is constructed with assumption that all nodes possess NGS less than the source node. To maintain a sense of load balance, children of each level's siblings are evenly distributed such that a node can not accept a second child, and so on, until all its siblings able to do so, have already acquired the same number of children. That is so logically done from left to right in a conceptual overlay tree. The records of the pair-wise approximate available bandwidths as measured by a pathChirp scheme at individual nodes are maintained. Evaluation measures as compared to other schemes – Bandwidth Aware multicaSt architecturE (BASE), Tree Building Control Protocol (TBCP), and Host Multicast Tree Protocol (HMTP) - have been conducted. This new scheme generally performs better in terms of trade-off between packet delivery ratio; link stress; control overhead; and end-to-end delays.

Keywords: Overlay multicast, Available bandwidth, Max-heapform overlay, Induced packet loss, Bandwidth-latency product, Node Gain Score (NGS).

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Authors: Mahesh Pal

Abstract:

This paper proposes to use ETM+ multispectral data and panchromatic band as well as texture features derived from the panchromatic band for land cover classification. Four texture features including one 'internal texture' and three GLCM based textures namely correlation, entropy, and inverse different moment were used in combination with ETM+ multispectral data. Two data sets involving combination of multispectral, panchromatic band and its texture were used and results were compared with those obtained by using multispectral data alone. A decision tree classifier with and without boosting were used to classify different datasets. Results from this study suggest that the dataset consisting of panchromatic band, four of its texture features and multispectral data was able to increase the classification accuracy by about 2%. In comparison, a boosted decision tree was able to increase the classification accuracy by about 3% with the same dataset.

Keywords: Internal texture; GLCM; decision tree; boosting; classification accuracy.

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Authors: S. Gupta, R. Chadha

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Five crystal modifications of water insoluble artesunate were generated by recrystallizing it from various solvents with improved physicochemical properties. These generated crystal forms were characterized to select the most potent and soluble form. SEM of all the forms showed changes in external shape leading them to be different morphologically. DSC thermograms of Form III and Form V showed broad endotherm peaks at 83.04oC and 76.96oC prior to melting fusion of drug respectively. Calculated weight loss in TGA revealed that Form III and Form V are methanol and acetone solvates respectively. However, few additional peaks were appeared in XRPD pattern in these two solvate forms. All forms exhibit exothermic behavior in buffer and two solvates display maximum ease of molecular release from the lattice. Methanol and acetone solvates were found to be most soluble forms and exhibited higher antimalarial efficacy showing higher survival rate (83.3%) after 30 days.

Keywords: Artesunate, Crystal modifications, in vivo studies, Recrystallization.

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Authors: Homayoon Zarshenas, Mahdi Bamdad, Hadi Grailu, Akbar A. Shakoori

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In this study we focus on improvement performance of a cue based Motor Imagery Brain Computer Interface (BCI). For this purpose, data fusion approach is used on results of different classifiers to make the best decision. At first step Distinction Sensitive Learning Vector Quantization method is used as a feature selection method to determine most informative frequencies in recorded signals and its performance is evaluated by frequency search method. Then informative features are extracted by packet wavelet transform. In next step 5 different types of classification methods are applied. The methodologies are tested on BCI Competition II dataset III, the best obtained accuracy is 85% and the best kappa value is 0.8. At final step ordered weighted averaging (OWA) method is used to provide a proper aggregation classifiers outputs. Using OWA enhanced system accuracy to 95% and kappa value to 0.9. Applying OWA just uses 50 milliseconds for performing calculation.

Keywords: BCI, EEG, Classifier, Fuzzy operator, OWA.

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Authors: Xiaoli Shen, Yuehui Chen

Abstract:

Interactions among proteins are the basis of various life events. So, it is important to recognize and research protein interaction sites. A control set that contains 149 protein molecules were used here. Then 10 features were extracted and 4 sample sets that contained 9 sliding windows were made according to features. These 4 sample sets were calculated by Radial Basis Functional neutral networks which were optimized by Particle Swarm Optimization respectively. Then 4 groups of results were obtained. Finally, these 4 groups of results were integrated by decision fusion (DF) and Genetic Algorithm based Selected Ensemble (GASEN). A better accuracy was got by DF and GASEN. So, the integrated methods were proved to be effective.

Keywords: protein interaction sites, features, sliding windows, radial basis functional neutral networks, genetic algorithm basedselected ensemble.

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