Search results for: HeterogeneousCare Networks

Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of acquiring new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used in this work is to analyze the dynamics of different brain areas during a cognitive activity to find the relationships between the other areas analyzed to understand the functioning of neural networks better. Also, the latest advances in neuroscience have revealed the exis-tence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neurodevelopmental difficulties for their subsequent assessment and therapy. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process, specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho-pedagogical plans that allow obtaining an optimal integral development of the affected people.

Keywords: dyscalculia, neurodevelopment, evoked potentials, learning disabilities, neural networks

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Authors: Namugenyi Ephrance Eunice, Julianne Sansa Otim, Marco Zennaro, Stephen D. Wolthusen

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The Internet of Things (IoT) is a network of connected data processing devices, mechanical and digital machinery, items, animals, or people that may send data across a network without requiring human-to-human or human-to-computer interaction. Each component has sensors that can pick up on specific phenomena, as well as processing software and other technologies that can link to and communicate with other systems and/or devices over the Internet or other communication networks and exchange data with them. IoT is increasingly being used in fields other than consumer electronics, such as public safety, emergency response, industrial automation, autonomous vehicles, the Internet of Medical Things (IoMT), and general environmental monitoring. Consumer-based IoT applications, like smart home gadgets and wearables, are also becoming more prevalent. This paper presents the main IoT deployment areas for environmental monitoring in developing regions and the backhaul options suitable for them based on a couple of related works. The study includes an overview of existing IoT deployments, the underlying communication architectures, protocols, and technologies that support them. This overview shows that Low Power Wireless Area Networks (LPWANs) are very well suited for monitoring environment architectures designed for remote locations. LoRa technology, particularly the LoRaWAN protocol, has an advantage over other technologies due to its low power consumption, adaptability, and suitable communication range. The current challenges of various architectures are discussed in detail, with the major issue identified as obstruction of communication paths by buildings, trees, hills, etc.

Keywords: Communication technologies, environmental monitoring, Internet of Things, IoT, IoT deployment challenges.

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Authors: S. Bouhouche, M. Lahreche, S. Ziani, J. Bast

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Modern manufacturing facilities are large scale, highly complex, and operate with large number of variables under closed loop control. Early and accurate fault detection and diagnosis for these plants can minimise down time, increase the safety of plant operations, and reduce manufacturing costs. Fault detection and isolation is more complex particularly in the case of the faulty analog control systems. Analog control systems are not equipped with monitoring function where the process parameters are continually visualised. In this situation, It is very difficult to find the relationship between the fault importance and its consequences on the product failure. We consider in this paper an approach to fault detection and analysis of its effect on the production quality using an adaptive centring and scaling in the pickling process in cold rolling. The fault appeared on one of the power unit driving a rotary machine, this machine can not track a reference speed given by another machine. The length of metal loop is then in continuous oscillation, this affects the product quality. Using a computerised data acquisition system, the main machine parameters have been monitored. The fault has been detected and isolated on basis of analysis of monitored data. Normal and faulty situation have been obtained by an artificial neural network (ANN) model which is implemented to simulate the normal and faulty status of rotary machine. Correlation between the product quality defined by an index and the residual is used to quality classification.

Keywords: Modeling, fault detection and diagnosis, parameters estimation, neural networks, Fault Detection and Diagnosis (FDD), pickling process.

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: Short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, Gain.

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Authors: Chapman Eze Nnadozie

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Cloud computing, a technology that is made possible through virtualization within networks represents a shift from the traditional ownership of infrastructure and other resources by distinct organization to a more scalable pattern in which computer resources are rented online to organizations on either as a pay-as-you-use basis or by subscription. In other words, cloud computing entails the renting of computing resources (such as storage space, memory, servers, applications, networks, etc.) by a third party to its clients on a pay-as-go basis. It is a new innovative technology that is globally embraced because of its renowned benefits, profound of which is its cost effectiveness on the part of organizations engaged with its services. In Nigeria, the services are provided either directly to companies mostly by the key IT players such as Microsoft, IBM, and Google; or in partnership with some other players such as Infoware, Descasio, and Sunnet. This action enables organizations to rent IT resources on a pay-as-you-go basis thereby salvaging them from wastages accruable on acquisition and maintenance of IT resources such as ownership of a separate data centre. This paper intends to appraise the challenges of cloud computing adoption in Nigeria, bearing in mind the country’s peculiarities’ in terms of infrastructural development. The methodologies used in this paper include the use of research questionnaires, formulated hypothesis, and the testing of the formulated hypothesis. The major findings of this paper include the fact that there are some addressable challenges to the adoption of cloud computing in Nigeria. Furthermore, the country will gain significantly if the challenges especially in the area of infrastructural development are well addressed. This is because the research established the fact that there are significant gains derivable by the adoption of cloud computing by organizations in Nigeria. However, these challenges can be overturned by concerted efforts in the part of government and other stakeholders.

Keywords: Cloud computing, data centre, infrastructure, IT resources, network, servers, virtualization.

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Authors: Karamitsos Ioannis, Orfanidis Konstantinos

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In this paper an analysis of blackouts in electric power transmission systems is implemented using a model and studied in simple networks with a regular topology. The proposed model describes load demand and network improvements evolving on a slow timescale as well as the fast dynamics of cascading overloads and outages.

Keywords: Blackout, Generator, Load, Power Load.

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Authors: Yasin Tadayonrad, Alassane Ballé Ndiaye

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Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading/unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is the loading/unloading capacity in each source/destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods (FMCG) industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on Python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.

Keywords: Supply chain management, transportation, multiple-multiple network, timeslots management, mathematical modeling, mixed integer programming.

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Authors: M. Almutairi, S. Hadjiloucas

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The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Keywords: Harmonics, passive filter, power factor, power quality.

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Authors: Alexandre Boum, Salomon Madinatou

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This paper aims at bringing a scientific contribution to the cardiac arrhythmia biomedical diagnosis systems; more precisely to the study of the amelioration of cardiac arrhythmia classification performance using artificial neural network, adaptive neuro-fuzzy and fuzzy inference systems classifiers. The purpose of this amelioration is to enable cardiologists to make reliable diagnosis through automatic cardiac arrhythmia analyzes and classifications based on high confidence classifiers. In this study, six classes of the most commonly encountered arrhythmias are considered: the Right Bundle Branch Block, the Left Bundle Branch Block, the Ventricular Extrasystole, the Auricular Extrasystole, the Atrial Fibrillation and the Normal Cardiac rate beat. From the electrocardiogram (ECG) extracted parameters, we constructed a matrix (360x360) serving as an input data sample for the classifiers based on neural networks and a matrix (1x6) for the classifier based on fuzzy logic. By varying three parameters (the quality of the neural network learning, the data size and the quality of the input parameters) the automatic classification permitted us to obtain the following performances: in terms of correct classification rate, 83.6% was obtained using the fuzzy logic based classifier, 99.7% using the neural network based classifier and 99.8% for the adaptive neuro-fuzzy based classifier. These results are based on signals containing at least 360 cardiac cycles. Based on the comparative analysis of the aforementioned three arrhythmia classifiers, the classifiers based on neural networks exhibit a better performance.

Keywords: Adaptive neuro-fuzzy, artificial neural network, cardiac arrythmias, fuzzy inference systems.

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Authors: Iman Zangeneh, Mostafa Moradi, Ali Mokhtarbaf

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The necessity of ever-increasing use of distributed data in computer networks is obvious for all. One technique that is performed on the distributed data for increasing of efficiency and reliablity is data rplication. In this paper, after introducing this technique and its advantages, we will examine some dynamic data replication. We will examine their characteristies for some overus scenario and the we will propose some suggestion for their improvement.

Keywords: data replication, data hiding, consistency, dynamicdata replication strategy

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x, and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts a motivating starting point. In this work, we extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zP , zN]. The zP component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zN component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach, coined Augmented Posterior CDE (AP-CDE), only requires a simple modification on the common normalizing flow framework, while significantly improving the interpretation of the latent component, since zP represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of x-related variations due to factors such as lighting condition and subject id, from the other random variations. Further, the experiments show that an unconditional NF neural network, based on an unsupervised model of z, such as Gaussian mixture, fails to generate interpretable results.

Keywords: Conditional density estimation, image generation, normalizing flow, supervised dimension reduction.

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Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: Continuous wavelet transform, convolution neural network, gated recurrent unit, health indicators, remaining useful life.

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Authors: Gianluca Cornetta, Abdellah Touhafi, David J. Santos, Jose Manuel Vazquez

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In this work a new platform for mobile-health systems is presented. System target application is providing decision support to rescue corps or military medical personnel in combat areas. Software architecture relies on a distributed client-server system that manages a wireless ad-hoc networks hierarchy in which several different types of client operate. Each client is characterized for different hardware and software requirements. Lower hierarchy levels rely in a network of completely custom devices that store clinical information and patient status and are designed to form an ad-hoc network operating in the 2.4 GHz ISM band and complying with the IEEE 802.15.4 standard (ZigBee). Medical personnel may interact with such devices, that are called MICs (Medical Information Carriers), by means of a PDA (Personal Digital Assistant) or a MDA (Medical Digital Assistant), and transmit the information stored in their local databases as well as issue a service request to the upper hierarchy levels by using IEEE 802.11 a/b/g standard (WiFi). The server acts as a repository that stores both medical evacuation forms and associated events (e.g., a teleconsulting request). All the actors participating in the diagnostic or evacuation process may access asynchronously to such repository and update its content or generate new events. The designed system pretends to optimise and improve information spreading and flow among all the system components with the aim of improving both diagnostic quality and evacuation process.

Keywords: IEEE 802.15.4 (ZigBee), IEEE 802.11 a/b/g (WiFi), distributed client-server systems, embedded databases, issue trackers, ad-hoc networks.

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Authors: Emanuel Teixeira, Anderson Ramos, Marisa Lourenço, Fernando J. Velez, Jon M. Peha

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This article discusses the benefit cost analysis aspects of millimetre wavebands (mmWaves) and Super High Frequency (SHF). The devaluation along the distance of the carrier-to-noise-plus-interference ratio with the coverage distance is assessed by considering two different path loss models, the two-slope urban micro Line-of-Sight (UMiLoS) for the SHF band and the modified Friis propagation model, for frequencies above 24 GHz. The equivalent supported throughput is estimated at the 5.62, 28, 38, 60 and 73 GHz frequency bands and the influence of carrier-to-noise-plus-interference ratio in the radio and network optimization process is explored. Mostly owing to the lessening caused by the behaviour of the two-slope propagation model for SHF band, the supported throughput at this band is higher than at the millimetre wavebands only for the longest cell lengths. The benefit cost analysis of these pico-cellular networks was analysed for regular cellular topologies, by considering the unlicensed spectrum. For shortest distances, we can distinguish an optimal of the revenue in percentage terms for values of the cell length, R ≈ 10 m for the millimeter wavebands and for longest distances an optimal of the revenue can be observed at R ≈ 550 m for the 5.62 GHz. It is possible to observe that, for the 5.62 GHz band, the profit is slightly inferior than for millimetre wavebands, for the shortest Rs, and starts to increase for cell lengths approximately equal to the ratio between the break-point distance and the co-channel reuse factor, achieving a maximum for values of R approximately equal to 550 m.

Keywords: 5G, millimetre wavebands, super high-frequency band, SINR, signal-to-interference-plus-noise ratio, cost benefit analysis.

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Authors: Jörg Linde, Ekaterina Buyko, Robert Altwasser, Udo Hahn, Reinhard Guthke

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Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.

Keywords: Pathogen, network inference, text-mining, Candida albicans, LASSO, mutual information, reverse engineering, linear regression, modelling.

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Authors: N.Vahdatifar, Ss.Mortazavi, R.Kianinezhad

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In this paper, a Neural Network based predictive DTC algorithm is proposed .This approach is used as an alternative to classical approaches .An appropriate riate Feed - forward network is chosen and based on its value of derivative electromagnetic torque ; optimal stator voltage vector is determined to be applied to the induction motor (by inverter). Moreover, an appropriate torque and flux observer is proposed.

Keywords: Neural Networks, Predictive DTC

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Authors: P. Sowa

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The new idea of analyze of power system failure with use of artificial neural network is proposed. An analysis of the possibility of simulating phenomena accompanying system faults and restitution is described. It was indicated that the universal model for the simulation of phenomena in whole analyzed range does not exist. The main classic method of search of optimal structure and parameter identification are described shortly. The example with results of calculation is shown.

Keywords: Dynamic equivalents, Network reduction, Neural networks, Power system analysis.

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: ANN, DWT, GLCM, KNN, ROI, artificial neural networks, discrete wavelet transform, gray-level co-occurrence matrix, k-nearest neighbor, region of interest.

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Authors: Ivaylo I. Atanasov, Evelina N.Pencheva

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This paper presents a mark-up approach to service creation in Next Generation Networks. The approach allows deriving added value from network functions exposed by Parlay/OSA (Open Service Access) interfaces. With OSA interfaces service logic scripts might be executed both on callrelated and call-unrelated events. To illustrate the approach XMLbased language constructions for data and method definitions, flow control, time measuring and supervision and database access are given and an example of OSA application is considered.

Keywords: Service creation, mark-up approach.

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Authors: Rade M. Ciric, Nikola Lj.Rajakovic

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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

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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

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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: Nancy Yaneth Gelvez García, Danilo Alfonso López Sarmiento

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This article presents the results of researchrelated to the assessment protocol weightedcumulative expected transmission time (WCETT)applied to cognitive radio networks.The development work was based on researchdone by different authors, we simulated a network,which communicates wirelessly, using a licensedchannel, through which other nodes are notlicensed, try to transmit during a given time nodeuntil the station's owner begins its transmission.

Keywords: Cognitive radio, ETT, WCETT

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Authors: Akim Borbuev, Francisco de León

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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: Semanur Soyyiğit Kaya, Ercan Eren

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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

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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: 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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