Search results for: sensor network.

Authors: Raquel Ramos López, Anissa El-Khattabi, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

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An increasing number of mobile devices with integrated cameras has meant that most digital video comes from these devices. These digital videos can be made anytime, anywhere and for different purposes. They can also be shared on the Internet in a short period of time and may sometimes contain recordings of illegal acts. The need to reliably trace the origin becomes evident when these videos are used for forensic purposes. This work proposes an algorithm to identify the brand and model of mobile device which generated the video. Its procedure is as follows: after obtaining the relevant video information, a classification algorithm based on sensor noise and Wavelet Transform performs the aforementioned identification process. We also present experimental results that support the validity of the techniques used and show promising results.

Keywords: Digital video, forensics analysis, key frame, mobile device, PRNU, sensor noise, source identification.

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Authors: E. Ramaraj, A. Padmapriya

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In large Internet backbones, Service Providers typically have to explicitly manage the traffic flows in order to optimize the use of network resources. This process is often referred to as Traffic Engineering (TE). Common objectives of traffic engineering include balance traffic distribution across the network and avoiding congestion hot spots. Raj P H and SVK Raja designed the Bayesian network approach to identify congestion hors pots in MPLS. In this approach for every node in the network the Conditional Probability Distribution (CPD) is specified. Based on the CPD the congestion hot spots are identified. Then the traffic can be distributed so that no link in the network is either over utilized or under utilized. Although the Bayesian network approach has been implemented in operational networks, it has a number of well known scaling issues. This paper proposes a new approach, which we call the Pragati (means Progress) Node Popularity (PNP) approach to identify the congestion hot spots with the network topology alone. In the new Pragati Node Popularity approach, IP routing runs natively over the physical topology rather than depending on the CPD of each node as in Bayesian network. We first illustrate our approach with a simple network, then present a formal analysis of the Pragati Node Popularity approach. Our PNP approach shows that for any given network of Bayesian approach, it exactly identifies the same result with minimum efforts. We further extend the result to a more generic one: for any network topology and even though the network is loopy. A theoretical insight of our result is that the optimal routing is always shortest path routing with respect to some considerations of hot spots in the networks.

Keywords: Conditional Probability Distribution, Congestion hotspots, Operational Networks, Traffic Engineering.

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Authors: Tanja Radu, Cormac Fay, King Tong Lau, Rhys Waite, Dermot Diamond

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The development of wearable sensing technologies is a great challenge which is being addressed by the Proetex FP6 project (www.proetex.org). Its main aim is the development of wearable sensors to improve the safety and efficiency of emergency personnel. This will be achieved by continuous, real-time monitoring of vital signs, posture, activity, and external hazards surrounding emergency workers. We report here the development of carbon dioxide (CO2) sensing boot by incorporating commercially available CO2 sensor with a wireless platform into the boot assembly. Carefully selected commercially available sensors have been tested. Some of the key characteristics of the selected sensors are high selectivity and sensitivity, robustness and the power demand. This paper discusses some of the results of CO2 sensor tests and sensor integration with wireless data transmission

Keywords: Proetex, gas sensing, wireless, wearable sensors, carbon dioxide

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Authors: Dorota Galeza

Abstract:

This paper argues that networks, such as the ECN and the American network, are affected by certain small events which are inherent to path dependence and preclude the full evolution towards efficiency. It is advocated that the American network is superior to the ECN in many respects due to its greater flexibility and longer history. This stems in particular from the creation of the American network, which was based on a small number of cases. Such a structure encourages further changes and modifications which are not necessarily radical. The ECN, by contrast, was established by legislative action, which explains its rigid structure and resistance to change. This paper is an attempt to transpose the superiority of the American network on to the ECN. It looks at concepts such as judicial cooperation, harmonisation of procedure, peer review and regulatory impact assessments (RIAs), and dispute resolution procedures.

Keywords: Antitrust, Competition, Networks, Path Dependence.

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Authors: G. Zazzaro, F.M. Pisano, G. Romano

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During last decades, worldwide researchers dedicated efforts to develop machine-based seismic Early Warning systems, aiming at reducing the huge human losses and economic damages. The elaboration time of seismic waveforms is to be reduced in order to increase the time interval available for the activation of safety measures. This paper suggests a Data Mining model able to correctly and quickly estimate dangerousness of the running seismic event. Several thousand seismic recordings of Japanese and Italian earthquakes were analyzed and a model was obtained by means of a Bayesian Network (BN), which was tested just over the first recordings of seismic events in order to reduce the decision time and the test results were very satisfactory. The model was integrated within an Early Warning System prototype able to collect and elaborate data from a seismic sensor network, estimate the dangerousness of the running earthquake and take the decision of activating the warning promptly.

Keywords: Bayesian Networks, Decision Support System, Magnitude Classification, Seismic Early Warning System

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Authors: Nilar Win Kyaw

Abstract:

In cryptography, confusion and diffusion are very important to get confidentiality and privacy of message in block ciphers and stream ciphers. There are two types of network to provide confusion and diffusion properties of message in block ciphers. They are Substitution- Permutation network (S-P network), and Feistel network. NLFS (Non-Linear feedback stream cipher) is a fast and secure stream cipher for software application. NLFS have two modes basic mode that is synchronous mode and self synchronous mode. Real random numbers are non-deterministic. R-box (random box) based on the dynamic properties and it performs the stochastic transformation of data that can be used effectively meet the challenges of information is protected from international destructive impacts. In this paper, a new implementation of stochastic transformation will be proposed.

Keywords: S-P network, Feistel network, R-block, stochastic transformation

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Authors: Kyung Hyun, Choi, Minh Ngoc, Nong, M. Asif Ali, Rehmani

Abstract:

This present paper proposes the modified Elastic Strip method for mobile robot to avoid obstacles with a real time system in an uncertain environment. The method deals with the problem of robot in driving from an initial position to a target position based on elastic force and potential field force. To avoid the obstacles, the robot has to modify the trajectory based on signal received from the sensor system in the sampling times. It was evident that with the combination of Modification Elastic strip and Pseudomedian filter to process the nonlinear data from sensor uncertainties in the data received from the sensor system can be reduced. The simulations and experiments of these methods were carried out.

Keywords: Collision avoidance, Avoidance obstacle, Elastic Strip, Real time collision avoidance.

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Authors: Devin Atkin, Orly Yadid-Pecht

Abstract:

The continual demands for increasing resolution and dynamic range in complimentary metal-oxide semiconductor (CMOS) image sensors have resulted in exponential increases in the amount of data that need to be read out of an image sensor, and existing readouts cannot keep up with this demand. Interesting approaches such as sparse and burst readouts have been proposed and show promise, but at considerable trade-offs in other specifications. To this end, we have begun designing and evaluating various readout topologies centered around an attempt to parallelize the sensor readout. In this paper, we have designed, simulated, and started testing a light-controlled oscillator topology with dual column and row readouts. We expect the parallel readout structure to offer greater speed and alleviate the trade-off typical in this topology, where slow pixels present a major framerate bottleneck.

Keywords: CMOS image sensors, high-speed capture, wide dynamic range, light controlled oscillator.

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Authors: Babar S. Kawish, Baber Aslam, Shoab A Khan

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In this paper we show that adjusting ART in accordance with static network scenario can substantially improve the performance of AODV by reducing control overheads. We explain the relationship of control overheads with network size and request patterns of the users. Through simulation we show that making ART proportionate to network static time reduces the amount of control overheads independent of network size and user request patterns.

Keywords: AODV, ART, MANET, Route Cache, TTL.

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Authors: Ahmad M. Awwad, Jehad Al-Sadi

Abstract:

The star network is one of the promising interconnection networks for future high speed parallel computers, it is expected to be one of the future-generation networks. The star network is both edge and vertex symmetry, it was shown to have many gorgeous topological proprieties also it is owns hierarchical structure framework. Although much of the research work has been done on this promising network in literature, it still suffers from having enough algorithms for load balancing problem. In this paper we try to work on this issue by investigating and proposing an efficient algorithm for load balancing problem for the star network. The proposed algorithm is called Star Clustered Dimension Exchange Method SCDEM to be implemented on the star network. The proposed algorithm is based on the Clustered Dimension Exchange Method (CDEM). The SCDEM algorithm is shown to be efficient in redistributing the load balancing as evenly as possible among all nodes of different factor networks.

Keywords: Interconnection networks, Load balancing, Star network.

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Authors: Vishwesh Kulkarni, Nikhil Bellarykar

Abstract:

Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets.

Keywords: Synthetic gene network, network identification, nonlinear modeling, optimization.

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Authors: Taweepol Suesut, Arjin Numsomran, Vittaya Tipsuwanporn

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This paper presents the communication network for machine vision system to implement to control systems and logistics applications in industrial environment. The real-time distributed over the network is very important for communication among vision node, image processing and control as well as the distributed I/O node. A robust implementation both with respect to camera packaging and data transmission has been accounted. This network consists of a gigabit Ethernet network and a switch with integrated fire-wall is used to distribute the data and provide connection to the imaging control station and IEC-61131 conform signal integration comprising the Modbus TCP protocol. The real-time and delay time properties each part on the network were considered and worked out in this paper.

Keywords: Distributed Real-Time Automation, Machine Visionand Ethernet.

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Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: Collision identification, fixed time, convex polyhedra, neural network, AMAXNET.

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Authors: S. Dhib, S. A. Addouche, T. Loukil, A. Elmhamedi

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In this paper, a reverse supply chain network is investigated for a decision making. This decision is surrounded by complex flows of returned products, due to the increasing quantity, the type of returned products and the variety of recovery option products (reuse, recycling, and refurbishment). The most important problem in the reverse logistic network (RLN) is to orient returned products to the suitable type of recovery option. However, returned products orientations from collect sources to the recovery disposition have not well considered in performance model. In this study, we propose a performance model for designing a network configuration on reverse logistics. Conceptual and analytical models are developed with taking into account operational, economic and environmental factors on designing network.

Keywords: Reverse logistics, Network design, Performance model, Open loop configuration.

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Authors: B. Cortés, A. Boza, D. Pérez, L. Cuenca

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The Internet of Things (IoT) field has been applied in industries with different purposes. Sensing Enterprise (SE) is an attribute of an enterprise or a network that allows it to react to business stimuli originating on the Internet. These fields have come into focus recently on the enterprises, and there is some evidence of the use and implications in supply chain management, while finding it as an interesting aspect to work on. This paper presents a revision and proposals of IoT applications in supply chain management.

Keywords: Internet of Things, Sensing Enterprises, Supply Chain Management, Industrial, Production Systems, Sensor.

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Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. Several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature, and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control.

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Authors: Deepali Virmani , Satbir Jain

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To meet the demands of wireless sensor networks (WSNs) where data are usually aggregated at a single source prior to transmitting to any distant user, there is a need to establish a tree structure inside any given event region. In this paper , a novel technique to create one such tree is proposed .This tree preserves the energy and maximizes the lifetime of event sources while they are constantly transmitting for data aggregation. The term Decentralized Lifetime Maximizing Tree (DLMT) is used to denote this tree. DLMT features in nodes with higher energy tend to be chosen as data aggregating parents so that the time to detect the first broken tree link can be extended and less energy is involved in tree maintenance. By constructing the tree in such a way, the protocol is able to reduce the frequency of tree reconstruction, minimize the amount of data loss ,minimize the delay during data collection and preserves the energy.

Keywords: branch energy, decentralized, energy level , lifetime, tree energy, wireless sensor networks.

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Authors: Yelena Chaiko

Abstract:

This paper presents a developed method for controlling multi-renewable energy generators. The control system depends basically on three sensors (wind anemometer, solar sensor, and voltage sensor). These sensors represent PLC-s analogue inputs. Controlling the output voltage supply can be achieved by an enhanced method of interlocking between the renewable energy generators, depending on those sensors and output contactors.

Keywords: Renewable, energy, control, model, generator.

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Authors: Hafizah Che Hasan, Fateen Nazwa Yusof, Maslina Daud

Abstract:

Internet of Things (IoT) is a powerful industry system, which end-devices are interconnected and automated, allowing the devices to analyze data and execute actions based on the analysis. The IoT technology leverages the technology of Radio-Frequency Identification (RFID) and Wireless Sensor Network (WSN), including mobile and sensor. These technologies contribute to the evolution of IoT. However, due to more devices are connected each other in the Internet, and data from various sources exchanged between things, confidentiality of the data becomes a major concern. This paper focuses on one of the major challenges in IoT; authentication, in order to preserve data integrity and confidentiality are in place. A few solutions are reviewed based on papers from the last few years. One of the proposed solutions is securing the communication between IoT devices and cloud servers with Elliptic Curve Cryptograhpy (ECC) based mutual authentication protocol. This solution focuses on Hyper Text Transfer Protocol (HTTP) cookies as security parameter.  Next proposed solution is using keyed-hash scheme protocol to enable IoT devices to authenticate each other without the presence of a central control server. Another proposed solution uses Physical Unclonable Function (PUF) based mutual authentication protocol. It emphasizes on tamper resistant and resource-efficient technology, which equals a 3-way handshake security protocol.

Keywords: Internet of Things, authentication, PUF ECC, keyed hash scheme protocol.

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Authors: Seok Hyoung Lee, Vladimir Shin

Abstract:

An optimal mean-square fusion formulas with scalar and matrix weights are presented. The relationship between them is established. The fusion formulas are compared on the continuous-time filtering problem. The basic differential equation for cross-covariance of the local errors being the key quantity for distributed fusion is derived. It is shown that the fusion filters are effective for multi-sensor systems containing different types of sensors. An example demonstrating the reasonable good accuracy of the proposed filters is given.

Keywords: Kalman filtering, fusion formula, multi-sensor, mean-square error.

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Authors: Jieh-Shian Young, Chan Wei Hsu

Abstract:

This paper mainly studies the analyses of parameters in the intersection collision avoidance (ICA) system based on the radar sensors. The parameters include the positioning errors, the repeat period of the radar sensor, the conditions of potential collisions of two cross-path vehicles, etc. The analyses of the parameters can provide the requirements, limitations, or specifications of this ICA system. In these analyses, the positioning errors will be increased as the measured vehicle approach the intersection. In addition, it is not necessary to implement the radar sensor in higher position since the positioning sensitivities become serious as the height of the radar sensor increases. A concept of the safety buffer distances for front and rear of the measured vehicle is also proposed. The conditions for potential collisions of two cross-path vehicles are also presented to facilitate the computation algorithm.

Keywords: Intersection Collision Avoidance (ICA), Positioning Errors, Radar Sensors, Sensitivity of Positioning.

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Authors: Kelvin Rozier, Vladimir E. Bondarenko

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It is known that an analog Hopfield neural network with time delay can generate the outputs which are similar to the human electroencephalogram. To gain deeper insights into the mechanisms of rhythm generation by the Hopfield neural networks and to study the effects of noise on their activities, we investigated the behaviors of the networks with symmetric and asymmetric interneuron connections. The neural network under the study consists of 10 identical neurons. For symmetric (fully connected) networks all interneuron connections aij = +1; the interneuron connections for asymmetric networks form an upper triangular matrix with non-zero entries aij = +1. The behavior of the network is described by 10 differential equations, which are solved numerically. The results of simulations demonstrate some remarkable properties of a Hopfield neural network, such as linear growth of outputs, dependence of synchronization properties on the connection type, huge amplification of oscillation by the external uniform noise, and the capability of the neural network to transform one type of noise to another.

Keywords: Chaos, Hopfield neural network, noise, synchronization

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Authors: Dewan Md. Farid, Nouria Harbi, Suman Ahmmed, Md. Zahidur Rahman, Chowdhury Mofizur Rahman

Abstract:

Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm for classification, which provides an optimal way to predict the class of an unknown example. It has been tested that one set of probability derived from data is not good enough to have good classification rate. In this paper, we proposed a new learning algorithm for mining network logs to detect network intrusions through naïve Bayesian classifier, which first clusters the network logs into several groups based on similarity of logs, and then calculates the prior and conditional probabilities for each group of logs. For classifying a new log, the algorithm checks in which cluster the log belongs and then use that cluster-s probability set to classify the new log. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions.

Keywords: Clustering, detection rate, false positive, naïveBayesian classifier, network intrusion detection.

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Authors: H. Shayeghi, M. Mahdavi

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Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e., expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using genetic algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. Finally, adequacy index could be defined and used to compare some designs that have different investment costs and adequacy rates. In this paper, the proposed idea has been tested on the Garvers network. The results show that the network will possess maximum efficiency economically.

Keywords: Adequacy Optimization, Transmission Expansion Planning, DCGA.

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Authors: U. Kairov, T. Karpenyuk, E. Ramanculov, A. Zinovyev

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The most common result of analysis of highthroughput data in molecular biology represents a global list of genes, ranked accordingly to a certain score. The score can be a measure of differential expression. Recent work proposed a new method for selecting a number of genes in a ranked gene list from microarray gene expression data such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. Here we present calculation results of relative connectivity of genes from META-OFTEN network and tentative biological interpretation of the most reproducible signal. The relative connectivity and inbetweenness values of genes from META-OFTEN network were estimated.

Keywords: Microarray, META-OFTEN, gene network.

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Authors: Areej Babiker Idris Babiker, Rosdiazli Ibrahim

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Gas chromatography (GC) is the most widely used technique in analytical chemistry. However, GC has high initial cost and requires frequent maintenance. This paper examines the feasibility and potential of using a neural network model as an alternative whenever GC is unvailable. It can also be part of system verification on the performance of GC for preventive maintenance activities. It shows the performance of MultiLayer Perceptron (MLP) with Backpropagation structure. Results demonstrate that neural network model when trained using this structure provides an adequate result and is suitable for this purpose. cm.

Keywords: Analyzer, Levenberg-Marquardt, Gas chromatography, Neural network

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Authors: K. Geetha, P. Thangaraj

Abstract:

Mobile Ad Hoc Networks (MANETs) is a collection of mobile devices forming a communication network without infrastructure. MANET is vulnerable to security threats due to network’s limited security, dynamic topology, scalability and the lack of central management. The Quality of Service (QoS) routing in such networks is limited by network breakage caused by node mobility or nodes energy depletions. The impact of node mobility on trust establishment is considered and its use to propagate trust through a network is investigated in this paper. This work proposes an enhanced Associativity Based Routing (ABR) with Fuzzy based Trust (Fuzzy- ABR) routing protocol for MANET to improve QoS and to mitigate network attacks.

Keywords: Mobile Ad hoc Networks (MANET), Associativity Based Routing (ABR), Fuzzy based Computed Trust.

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Authors: K.Sedhuraman, S.Himavathi, A.Muthuramalingam

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The performance of sensor-less controlled induction motor drive depends on the accuracy of the estimated speed. Conventional estimation techniques being mathematically complex require more execution time resulting in poor dynamic response. The nonlinear mapping capability and powerful learning algorithms of neural network provides a promising alternative for on-line speed estimation. The on-line speed estimator requires the NN model to be accurate, simpler in design, structurally compact and computationally less complex to ensure faster execution and effective control in real time implementation. This in turn to a large extent depends on the type of Neural Architecture. This paper investigates three types of neural architectures for on-line speed estimation and their performance is compared in terms of accuracy, structural compactness, computational complexity and execution time. The suitable neural architecture for on-line speed estimation is identified and the promising results obtained are presented.

Keywords: Sensorless IM drives, rotor speed estimators, artificial neural network, feed- forward architecture, single neuron cascaded architecture.

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Authors: Juha Kettunen

Abstract:

This study analyzes the quality and the size of the strategic network of higher education institutions. The study analyses the concept of fitness for purpose in quality assurance. It also analyses the transaction costs of networking that have consequences on the number of members in the network. Empirical evidence is presented of the Consortium on Applied Research and Professional Education, which is a European strategic network of six higher education institutions. The results of the study support the argument that the number of members in the strategic network should be relatively small to provide high quality results. The practical importance is that networking has been able to promote international research and development projects. The results of this study are important for those who want to design and improve international networks in higher education.

Keywords: Higher education, network, research and development, strategic management.

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Authors: Manoj Kumar, A.K. Verma, A. Srividya

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Time varying network induced delays in networked control systems (NCS) are known for degrading control system-s quality of performance (QoP) and causing stability problems. In literature, a control method employing modeling of communication delays as probability distribution, proves to be a better method. This paper focuses on modeling of network induced delays as probability distribution. CAN and MIL-STD-1553B are extensively used to carry periodic control and monitoring data in networked control systems. In literature, methods to estimate only the worst-case delays for these networks are available. In this paper probabilistic network delay model for CAN and MIL-STD-1553B networks are given. A systematic method to estimate values to model parameters from network parameters is given. A method to predict network delay in next cycle based on the present network delay is presented. Effect of active network redundancy and redundancy at node level on network delay and system response-time is also analyzed.

Keywords: NCS (networked control system), delay analysis, response-time distribution, worst-case delay, CAN, MIL-STD-1553B, redundancy

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