Search results for: network pattern models

Authors: Lee P. Leon, Raymond Charles

Abstract:

This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: Aggregate angularity, asphalt concrete, permanent deformation, rutting prediction.

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Authors: Dilek Özdemir, Gül Tekin Temur

Abstract:

In Supply Chain Management (SCM), strengthening partnerships with suppliers is a significant factor for enhancing competitiveness. Hence, firms increasingly emphasize supplier evaluation processes. Supplier evaluation systems are basically developed in terms of criteria such as quality, cost, delivery, and flexibility. Because there are many variables to be analyzed, this process becomes hard to execute and needs expertise. On this account, this study aims to develop an expert system on supplier evaluation process by designing Artificial Neural Network (ANN) that is supported with Data Envelopment Analysis (DEA). The methods are applied on the data of 24 suppliers, which have longterm relationships with a medium sized company from German Iron and Steel Industry. The data of suppliers consists of variables such as material quality (MQ), discount of amount (DOA), discount of cash (DOC), payment term (PT), delivery time (DT) and annual revenue (AR). Meanwhile, the efficiency that is generated by using DEA is added to the supplier evaluation system in order to use them as system outputs.

Keywords: Artificial Neural Network (ANN), DataEnvelopment Analysis (DEA), Supplier Evaluation System.

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Authors: Y. Harold Robinson, M. Rajaram

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Designing cost-efficient, secure network protocols for Wireless Sensor Networks (WSNs) is a challenging problem because sensors are resource-limited wireless devices. Security services such as authentication and improved pairwise key establishment are critical to high efficient networks with sensor nodes. For sensor nodes to correspond securely with each other efficiently, usage of cryptographic techniques is necessary. In this paper, two key predistribution schemes that enable a mobile sink to establish a secure data-communication link, on the fly, with any sensor nodes. The intermediate nodes along the path to the sink are able to verify the authenticity and integrity of the incoming packets using a predicted value of the key generated by the sender’s essential power. The proposed schemes are based on the pairwise key with the mobile sink, our analytical results clearly show that our schemes perform better in terms of network resilience to node capture than existing schemes if used in wireless sensor networks with mobile sinks.

Keywords: Wireless Sensor Networks, predistribution scheme, cryptographic techniques.

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Authors: Magdi M. Nabi, Ding-Li Yu

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This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.

Keywords: Chylla-Haase reactor, RBF neural network modelling, model predictive control.

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Authors: Md. Ataullah, Naveen Chauhan

Abstract:

The Address Resolution Protocol (ARP) is used by computers to map logical addresses (IP) to physical addresses (MAC). However ARP is an all trusting protocol and is stateless which makes it vulnerable to many ARP cache poisoning attacks such as Man-in-the-Middle (MITM) and Denial of service (DoS) attacks. These flaws result in security breaches thus weakening the appeal of the computer for exchange of sensitive data. In this paper we describe ARP, outline several possible ARP cache poisoning attacks and give the detailed of some attack scenarios in network having both wired and wireless hosts. We have analyzed each of proposed solutions, identify their strengths and limitations. Finally get that no solution offers a feasible solution. Hence, this paper presents an efficient and secure version of ARP that is able to cope up with all these types of attacks and is also a feasible solution. It is a stateful protocol, by storing the information of the Request frame in the ARP cache, to reduce the chances of various types of attacks in ARP. It is more efficient and secure by broadcasting ARP Reply frame in the network and storing related entries in the ARP cache each time when communication take place.

Keywords: ARP cache poisoning, MITM, DoS

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: Crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest.

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Authors: Ahmed ElShafee, Karim Alaa Hamed

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This paper presents a design and prototype implementation of new home automation system that uses WiFi technology as a network infrastructure connecting its parts. The proposed system consists of two main components; the first part is the server (web server), which presents system core that manages, controls, and monitors users- home. Users and system administrator can locally (LAN) or remotely (internet) manage and control system code. Second part is hardware interface module, which provides appropriate interface to sensors and actuator of home automation system. Unlike most of available home automation system in the market the proposed system is scalable that one server can manage many hardware interface modules as long as it exists on WiFi network coverage. System supports a wide range of home automation devices like power management components, and security components. The proposed system is better from the scalability and flexibility point of view than the commercially available home automation systems.

Keywords: Home automation, Wireless LAN, WiFi, MicroControllers

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Authors: Ouadoudi Zytoune, Youssef Fakhri, Driss Aboutajdine

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This paper focuses on reducing the power consumption of wireless sensor networks. Therefore, a communication protocol named LEACH (Low-Energy Adaptive Clustering Hierarchy) is modified. We extend LEACHs stochastic cluster-head selection algorithm by a modifying the probability of each node to become cluster-head based on its required energy to transmit to the sink. We present an efficient energy aware routing algorithm for the wireless sensor networks. Our contribution consists in rotation selection of clusterheads considering the remoteness of the nodes to the sink, and then, the network nodes residual energy. This choice allows a best distribution of the transmission energy in the network. The cluster-heads selection algorithm is completely decentralized. Simulation results show that the energy is significantly reduced compared with the previous clustering based routing algorithm for the sensor networks.

Keywords: Wireless Sensor Networks, Energy efficiency, WirelessCommunications, Clustering-based algorithm.

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Authors: Claude Takenga, Rolf-Dietrich Berndt, Erling Si, Markus Diem, Guohui Qiao, Melanie Gau, Michael Brandstoetter, Martin Kampel, Michael Dworzak

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In patients with acute lymphoblastic leukaemia (ALL), treatment response is increasingly evaluated with minimal residual disease (MRD) analyses. Flow Cytometry (FCM) is a fast and sensitive method to detect MRD. However, the interpretation of these multi-parametric data requires intensive operator training and experience. This paper presents a pipeline-software, as a ready-to-use FCM-based MRD-assessment tool for the daily clinical practice for patients with ALL. The new tool increases accuracy in assessment of FCM-MRD in samples which are difficult to analyse by conventional operator-based gating since computer-aided analysis potentially has a superior resolution due to utilization of the whole multi-parametric FCM-data space at once instead of step-wise, two-dimensional plot-based visualization. The system developed as a telemedical network reduces the work-load and lab-costs, staff-time needed for training, continuous quality control, operator-based data interpretation. It allows dissemination of automated FCM-MRD analysis to medical centres which have no established expertise for the benefit of an even larger community of diseased children worldwide. We established a telemedical network system for analysis and clinical follow-up and treatment monitoring of Leukaemia. The system is scalable and adapted to link several centres and laboratories worldwide.

Keywords: Data security, flow cytometry, leukaemia, telematics platform, telemedicine.

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Authors: M. D. Nayeri, M. M. Mashhadi, K. Mohajeri

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Defining strategic position of the organizations within the industry environment is one of the basic and most important phases of strategic planning to which extent that one of the fundamental schools of strategic planning is the strategic positioning school. In today-s knowledge-based economy and dynamic environment, it is essential for universities as the centers of education, knowledge creation and knowledge worker evolvement. Till now, variant models with different approaches to strategic positioning are deployed in defining the strategic position within the various industries. Balanced Scorecard as one of the powerful models for strategic positioning, analyzes all aspects of the organization evenly. In this paper with the consideration of BSC strength in strategic evaluation, it is used for analyzing the environmental position of the best-s Iranian Business Schools. The results could be used in developing strategic plans for these schools as well as other Iranian Management and Business Schools.

Keywords: Strategic planning, Strategic position, Balancedscorecard, Higher education institutions.

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Authors: Ahmed Hossam_ ElDin, Ahmed Farag, Ibrahim Madi., Hanaa Karawia

Abstract:

In our modern society electricity is vital to our health, safety, comfort and well-being. While our daily use of electricity is often taken for granted, public concern has arisen about potential adverse health effects from electric and magnetic – electromagnetic – fields (EMFs) produced by our use of electricity. This paper aims to compare between the measured magnetic field values and the simulated models for the indoor medium to low voltage (MV/LV) distribution substations. To calculate the magnetic flux density in the substations, interactive software SUBCALC is used which is based on closed form solution of the Biot-Savart law with 3D conductor model. The comparison between the measured values and the simulated models was acceptable. However there were some discrepancies, as expected, may be due to the current variation during measurements.

Keywords: Distribution substation, magnetic field, measurement, simulation.

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

Abstract:

In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: Agricultural object detection, Deep learning, machine vision, YOLO family.

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Authors: Mohd. Khairil Sailan, Rosilah Hassan, Zuhaizal Zulkifli

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Internet Protocol version 4 (IPv4) address is decreasing and a rapid transition method to the next generation IP address (IPv6) should be established. This study aims to evaluate and select the best performance of the IPv6 address network transitionmechanisms, such as IPv4/IPv6 dual stack, transport Relay Translation (TRT) and Reverse Proxy with additional features. It is also aim to prove that faster access can be done while ensuring optimal usage of available resources used during the test and actual implementation. This study used two test methods such asInternet Control Message Protocol (ICMP)ping and ApacheBenchmark (AB) methodsto evaluate the performance.Performance metrics for this study include aspects ofaverageaccessin one second,time takenfor singleaccess,thedata transfer speed and the costof additional requirements.Reverse Proxy with Caching featureis the most efficientmechanism because of it simpler configurationandthe best performerfrom the test conducted.

Keywords: IPv4, IPv6, network transition, apache benchmark andreverse proxy

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Authors: Mohamed Ghorab

Abstract:

Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO₂ optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO₂ emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO₂ emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.  

Keywords: Distributed energy resources, network energy system, optimization, microgeneration system.

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Authors: Sadaf Sahar, Usman Qamar, Sadaf Ayaz

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In the software development lifecycle, the quality prediction techniques hold a prime importance in order to minimize future design errors and expensive maintenance. There are many techniques proposed by various researchers, but with the increasing complexity of the software lifecycle model, it is crucial to develop a flexible system which can cater for the factors which in result have an impact on the quality of the end product. These factors include properties of the software development process and the product along with its operation conditions. In this paper, a neural network (perceptron) based software quality prediction technique is proposed. Using this technique, the stakeholders can predict the quality of the resulting software during the early phases of the lifecycle saving time and resources on future elimination of design errors and costly maintenance. This technique can be brought into practical use using successful training.

Keywords: Software quality, fuzzy logic, perceptron, prediction.

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Authors: R. Sridevi, G. Jagajothi

Abstract:

Artificial Immune Systems (AIS), inspired by the human immune system, are algorithms and mechanisms which are self-adaptive and self-learning classifiers capable of recognizing and classifying by learning, long-term memory and association. Unlike other human system inspired techniques like genetic algorithms and neural networks, AIS includes a range of algorithms modeling on different immune mechanism of the body. In this paper, a mechanism of a human immune system based on apoptosis is adopted to build an Intrusion Detection System (IDS) to protect computer networks. Features are selected from network traffic using Fisher Score. Based on the selected features, the record/connection is classified as either an attack or normal traffic by the proposed methodology. Simulation results demonstrates that the proposed AIS based on apoptosis performs better than existing AIS for intrusion detection.

Keywords: Apoptosis, Artificial Immune System (AIS), Fisher Score, KDD dataset, Network intrusion detection.

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Authors: M. Vatani, A. R. Rahimi, F. Brazandeh, A. Sanati nezhad

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Although the STL (stereo lithography) file format is widely used as a de facto industry standard in the rapid prototyping industry due to its simplicity and ability to tessellation of almost all surfaces, but there are always some defects and shortcoming in their usage, which many of them are difficult to correct manually. In processing the complex models, size of the file and its defects grow extremely, therefore, correcting STL files become difficult. In this paper through optimizing the exiting algorithms, size of the files and memory usage of computers to process them will be reduced. In spite of type and extent of the errors in STL files, the tail-to-head searching method and analysis of the nearest distance between tails and heads techniques were used. As a result STL models sliced rapidly, and fully closed contours produced effectively and errorless.

Keywords: Layer manufacturing, STL files, slicing algorithm, nearest distance analysis.

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Authors: T.P. Sharma

Abstract:

Querying a data source and routing data towards sink becomes a serious challenge in static wireless sensor networks if sink and/or data source are mobile. Many a times the event to be observed either moves or spreads across wide area making maintenance of continuous path between source and sink a challenge. Also, sink can move while query is being issued or data is on its way towards sink. In this paper, we extend our already proposed Grid Based Data Dissemination (GBDD) scheme which is a virtual grid based topology management scheme restricting impact of movement of sink(s) and event(s) to some specific cells of a grid. This obviates the need for frequent path modifications and hence maintains continuous flow of data while minimizing the network energy consumptions. Simulation experiments show significant improvements in network energy savings and average packet delay for a packet to reach at sink.

Keywords: Mobility in WSNs, virtual grid, GBDD, clustering.

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Authors: Quy Dang Nguyen, Reza Nakhaie Jazar

Abstract:

The paper investigates vibration dynamics of tire-road separation for a quarter-car model; this separation model is developed to be close to the real situation considering the tire is able to separate from the ground plane. A set of piecewise linear mathematical models is developed and matches the in-contact and no-contact states to be considered as mother models for further investigations. The bound dynamics are numerically simulated in the time response and phase portraits. The separation analysis may determine which values of suspension parameters can delay and avoid the no-contact phenomenon, which results in improving ride comfort and eliminating the potentially dangerous oscillation. Finally, model verification is carried out in the MSC-ADAMS environment.

Keywords: Quarter-car vibrations, tire-road separation, separation analysis, separation dynamics, ride comfort, ADAMS validation.

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Authors: Shahram Naghizadeh Raeisi, Ali Alghooneh

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Using membrane technology and modification of milk protein structural properties, a lactose free yogurt was developed. The functional, textural and structural properties of the sample were evaluated and compared with the commercial ones. Results showed that the modification of protein in high fat set yogurt resulted in 11.55%, 18%, 20.21% and 7.08% higher hardness, consistency, water holding capacity, and shininess values compared with the control one. Furthermore, these indices of modified low fat set yogurt were 21.40%, 25.41%, 28.15% & 10.58% higher than the control one, which could be related to the gel network microstructural properties in yogurt formulated with modified protein. In this way, in comparison with the control one, the index of linkage strength (A), the number of linkages (z), and time scale of linkages (λrel) of the high fat modified yogurt were 22.10%, 50.68%, 21.82% higher than the control one; whereas, the average linear distance between two adjacent crosslinks (ξ), was 16.77% lower than the control one. For low fat modified yogurt, A, z, λrel, and ξ indices were 34.30%, 61.70% and 42.60% higher and 19.20% lower than the control one, respectively. The shelf life of modified yogurt was extended to 10 weeks in the refrigerator, while, the control set yogurt had a 3 weeks shelf life. The acidity of high fat and low fat modified yogurts increased from 76 to 84 and 72 to 80 Dornic degrees during 10 weeks of storage, respectively, whereas for control high fat and low fat yogurts they increased from 82 to 122 and 77 to 112 Dornic degrees, respectively. This behavior could be due to the elimination of microorganism’s source of energy in modified yogurt. Furthermore, the calories of high fat and low fat lactose free yogurts were 25% and 40% lower than their control samples, respectively. Generally, results showed that the lactose free yogurt with modified protein, despite of 1% lower protein content than the control one, showed better functional properties, nutritional properties, network parameters, and shelf stability, which could be promising in the set yogurt industry.

Keywords: Lactose free, low calorie, network properties, protein modification.

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Authors: U. Sakthi, R. Hemalatha, R. S. Bhuvaneswaran

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In Virtual organization, Knowledge Discovery (KD) service contains distributed data resources and computing grid nodes. Computational grid is integrated with data grid to form Knowledge Grid, which implements Apriori algorithm for mining association rule on grid network. This paper describes development of parallel and distributed version of Apriori algorithm on Globus Toolkit using Message Passing Interface extended with Grid Services (MPICHG2). The creation of Knowledge Grid on top of data and computational grid is to support decision making in real time applications. In this paper, the case study describes design and implementation of local and global mining of frequent item sets. The experiments were conducted on different configurations of grid network and computation time was recorded for each operation. We analyzed our result with various grid configurations and it shows speedup of computation time is almost superlinear.

Keywords: Association rule, Grid computing, Knowledge grid, Mobility prediction.

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Authors: Aissam Belghiat, Mustapha Bourahla

Abstract:

Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared on several regards such as the classes and associations. In this paper, we have to profit from convergence between UML and OWL to propose an approach based on Meta-Modelling and Graph Grammars and registered in the MDA architecture for the automatic generation of OWL ontologies from UML class diagrams. The transformation is based on transformation rules; the level of abstraction in these rules is close to the application in order to have usable ontologies. We illustrate this approach by an example.

Keywords: ATOM3, MDA, Ontology, OWL, UML

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Authors: Z. Heirany, M. Ghaemian

Abstract:

Behavior of dams against the seismic loads has been studied by many researchers. Most of them proposed new numerical methods to investigate the dam safety. In this paper, to study the effect of nonlinear parameters of concrete in gravity dams, a twodimensional approach was used including the finite element method, staggered method and smeared crack approach. Effective parameters in the models are physical properties of concrete such as modulus of elasticity, tensile strength and specific fracture energy. Two different models were used in foundation (mass-less and massed) in order to determine the seismic response of concrete gravity dams. Results show that when the nonlinear analysis includes the dam- foundation interaction, the foundation-s mass, flexibility and radiation damping are important in gravity dam-s response.

Keywords: Numerical methods; concrete gravity dams; finiteelement method; boundary condition

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Authors: Manasjyoti Bhuyan, Kandarpa Kumar Sarma, Hirendra Das

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Signature represents an individual characteristic of a person which can be used for his / her validation. For such application proper modeling is essential. Here we propose an offline signature recognition and verification scheme which is based on extraction of several features including one hybrid set from the input signature and compare them with the already trained forms. Feature points are classified using statistical parameters like mean and variance. The scanned signature is normalized in slant using a very simple algorithm with an intention to make the system robust which is found to be very helpful. The slant correction is further aided by the use of an Artificial Neural Network (ANN). The suggested scheme discriminates between originals and forged signatures from simple and random forgeries. The primary objective is to reduce the two crucial parameters-False Acceptance Rate (FAR) and False Rejection Rate (FRR) with lesser training time with an intension to make the system dynamic using a cluster of ANNs forming a multiple classifier system.

Keywords: offline, algorithm, FAR, FRR, ANN.

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Authors: Z. Briqech, M. Abousetta

Abstract:

Luneberg lens is a new generation of antennas that is developed in the last few years and inserts itself strongly in Microwaves, Communications and Telescopes area. The idea of this research is to improve the radiation pattern by decreasing the side lobes and increasing the main lobe. The new design is proposed to work in the X-band. The simulated result and analysis are presented.

Keywords: Communications, Microwaves, lens Antenna, Lunberg Lens Antenna.

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Authors: Junghoon Lee, Gyung-Leen Park, Ho-Young Kwak, Cheol Min Kim

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This paper presents the design and implements the prototype of an intelligent data processing framework in ubiquitous sensor networks. Much focus is put on how to handle the sensor data stream as well as the interoperability between the low-level sensor data and application clients. Our framework first addresses systematic middleware which mitigates the interaction between the application layer and low-level sensors, for the sake of analyzing a great volume of sensor data by filtering and integrating to create value-added context information. Then, an agent-based architecture is proposed for real-time data distribution to efficiently forward a specific event to the appropriate application registered in the directory service via the open interface. The prototype implementation demonstrates that our framework can host a sophisticated application on the ubiquitous sensor network and it can autonomously evolve to new middleware, taking advantages of promising technologies such as software agents, XML, cloud computing, and the like.

Keywords: sensor network, intelligent farm, middleware, event detection

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Authors: Yasser F. Hassan, Marwa Abdeen, Mustafa Fahmy

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In recent years multi-agent systems have emerged as one of the interesting architectures facilitating distributed collaboration and distributed problem solving. Each node (agent) of the network might pursue its own agenda, exploit its environment, develop its own problem solving strategy and establish required communication strategies. Within each node of the network, one could encounter a diversity of problem-solving approaches. Quite commonly the agents can realize their processing at the level of information granules that is the most suitable from their local points of view. Information granules can come at various levels of granularity. Each agent could exploit a certain formalism of information granulation engaging a machinery of fuzzy sets, interval analysis, rough sets, just to name a few dominant technologies of granular computing. Having this in mind, arises a fundamental issue of forming effective interaction linkages between the agents so that they fully broadcast their findings and benefit from interacting with others.

Keywords: Granular computing, rough sets, agents, traffic system.

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Authors: Elizabeth B. Varghese, M. Wilscy

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A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.

Keywords: Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.

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Authors: Mohammad Syuhaimi Ab-Rahman, Mohd Hadi Guna Safnal, Mohd Hazwan Harun, Mohd.Saiful Dzulkefly Zan, Kasmiran Jumari

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Optical 1x12 fused-taper-twisted polymer optical fiber (POF) couplers has been fabricated by a perform technique. Characterization of the coupler which proposed to be used in passive night vision application to tracking a blind sport area was reported. During the development process of fused-taper-twisted POF couplers was carried out, red LED fully utilized to be injected into the couplers to test the quality of fabricated couplers. Some characterization parameters, such as optical output power, POFs attenuation characteristics and power losses on the network were observed. The maximum output power efficiency of the coupler is about 40%, but it can be improved gradually through experience and practice.

Keywords: polymer optical fiber (POF), customer-made, fused-taper-twisted fiber, optical coupler, small world communication, home network.

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

Abstract:

Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.

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