Search results for: Network Theory

Authors: Maryam Jamela Ismail, Rosdiazli Ibrahim, Idris Ismail

Abstract:

In the oil and gas industry, energy prediction can help the distributor and customer to forecast the outgoing and incoming gas through the pipeline. It will also help to eliminate any uncertainties in gas metering for billing purposes. The objective of this paper is to develop Neural Network Model for energy consumption and analyze the performance model. This paper provides a comprehensive review on published research on the energy consumption prediction which focuses on structures and the parameters used in developing Neural Network models. This paper is then focused on the parameter selection of the neural network prediction model development for energy consumption and analysis on the result. The most reliable model that gives the most accurate result is proposed for the prediction. The result shows that the proposed neural network energy prediction model is able to demonstrate an adequate performance with least Root Mean Square Error.

Keywords: Energy Prediction, Multilayer Feedforward, Levenberg-Marquardt, Root Mean Square Error (RMSE)

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Authors: Vrince Vimal, Madhav J. Nigam

Abstract:

Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime.

Keywords: WSN, random deployment, clustering, isolated nodes, network lifetime.

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Authors: F. Titouna, S. Benferhat, K. Aksa, C. Titouna

Abstract:

A traffic light gives security from traffic congestion,reducing the traffic jam, and organizing the traffic flow. Furthermore,increasing congestion level in public road networks is a growingproblem in many countries. Using Intelligent Transportation Systemsto provide emergency vehicles a green light at intersections canreduce driver confusion, reduce conflicts, and improve emergencyresponse times. Nowadays, the technology of wireless sensornetworks can solve many problems and can offer a good managementof the crossroad. In this paper, we develop a new approach based onthe technique of clustering and the graphical possibilistic fusionmodeling. So, the proposed model is elaborated in three phases. Thefirst one consists to decompose the environment into clusters,following by the fusion intra and inter clusters processes. Finally, wewill show some experimental results by simulation that proves theefficiency of our proposed approach.KeywordsTraffic light, Wireless sensor network, Controller,Possibilistic network/Bayesain network.

Keywords: Traffic light, Wireless sensor network, Controller, Possibilistic network/Bayesain network.

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Authors: T. T. Manikandan, Rajeev Sukumaran

Abstract:

Coral Reefs are very important for the majority of marine ecosystems. But, such vital species are under major threat due to the factors such as ocean acidification, overfishing, and coral bleaching. To conserve the coral reefs, reef restoration activities are carried out across the world. After reef restoration, various parameters have to be monitored in order to ensure the overall effectiveness of the reef restoration. Underwater Wireless Sensor Network (UWSN) based  monitoring is widely adopted for such long monitoring activities. Since monitoring of coral reef restoration activities is time sensitive, the QoS guarantee offered by the network with respect to delay is vital. So this research focuses on the analytical modeling of network layer delay using Stochastic Network Calculus (SNC). The core focus of the proposed model will be on the analysis of stochastic dependencies between the network flow and deriving the stochastic delay bounds for the flows that traverse in tandem in UWSNs. The derived analytical bounds are evaluated for their effectiveness using discrete event simulations.

Keywords: Coral Reef Restoration, SNC, SFA, PMOO, Tandem of Queues, Delay Bound.

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Authors: Raja Das, M. K. Pradhan

Abstract:

This paper presents a comparative study between two neural network models namely General Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to estimate radial overcut produced during Electrical Discharge Machining (EDM). Four input parameters have been employed: discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and discharge voltage (V). Recently, artificial intelligence techniques, as it is emerged as an effective tool that could be used to replace time consuming procedures in various scientific or engineering applications, explicitly in prediction and estimation of the complex and nonlinear process. The both networks are trained, and the prediction results are tested with the unseen validation set of the experiment and analysed. It is found that the performance of both the networks are found to be in good agreement with average percentage error less than 11% and the correlation coefficient obtained for the validation data set for GRNN and BPNN is more than 91%. However, it is much faster to train GRNN network than a BPNN and GRNN is often more accurate than BPNN. GRNN requires more memory space to store the model, GRNN features fast learning that does not require an iterative procedure, and highly parallel structure. GRNN networks are slower than multilayer perceptron networks at classifying new cases.

Keywords: Electrical-discharge machining, General Regression Neural Network, Back-propagation Neural Network, Radial Overcut.

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Authors: Khaled. M. Faqih

Abstract:

A model of vortex wake is suggested to determine the induced power during animal hovering flight. The wake is modeled by a series of equi-spaced rigid rectangular vortex plates, positioned horizontally and moving vertically downwards with identical speeds; each plate is generated during powering of the functionally wing stroke. The vortex representation of the wake considered in the current theory allows a considerable loss of momentum to occur. The current approach accords well with the nature of the wingbeat since it considers the unsteadiness in the wake as an important fluid dynamical characteristic. Induced power in hovering is calculated as the aerodynamic power required to generate the vortex wake system. Specific mean induced power to mean wing tip velocity ratio is determined by solely the normal spacing parameter (f) for a given wing stroke amplitude. The current theory gives much higher specific induced power estimate than anticipated by classical methods.

Keywords: vortex theory, hovering flight, induced power, Prandlt's tip theory.

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Authors: Su Hyung Jo, Ki Young Kim, Sang Ho Lee

Abstract:

Security management has changed from the management of security equipments and useful interface to manager. It analyzes the whole security conditions of network and preserves the network services from attacks. Secure router technology has security functions, such as intrusion detection, IPsec(IP Security) and access control, are applied to legacy router for secure networking. It controls an unauthorized router access and detects an illegal network intrusion. This paper relates to a security engine management of router based on a security policy, which is the definition of security function against a network intrusion. This paper explains the security policy and designs the structure of security engine management framework.

Keywords: Policy server, security engine, security management, security policy

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Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: Artificial neural network, EDM, metal removal rate, modeling, surface roughness.

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Authors: Fangfang Chen, Chunlu Wang, Zhihong Tian, Shuyuan Jin, Tianle Zhang

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Attack graph is an integral part of modeling the overview of network security. System administrators use attack graphs to determine how vulnerable their systems are and to determine what security measures to deploy to defend their systems. Previous methods on AGG(attack graphs generation) are aiming at the whole network, which makes the process of AGG complex and non-scalable. In this paper, we propose a new approach which is simple and scalable to AGG by decomposing the whole network into atomic domains. Each atomic domain represents a host with a specific privilege. Then the process for AGG is achieved by communications among all the atomic domains. Our approach simplifies the process of design for the whole network, and can gives the attack graphs including each attack path for each host, and when the network changes we just carry on the operations of corresponding atomic domains which makes the process of AGG scalable.

Keywords: atomic domain, vulnerability, attack graphs, generation, computer security

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Authors: Eda Gök

Abstract:

Peridynamics is a new modeling concept of non-local interactions for solid structures. The formulations of Peridynamic (PD) theory are based on integral equations rather than differential equations. Through, undefined equations of associated problems are avoided. PD theory might be defined as continuum version of molecular dynamics. The medium is usually modeled with mass particles bonded together. Particles interact with each other directly across finite distances through central forces named as bonds. The main assumption of this theory is that the body is composed of material points which interact with other material points within a finite distance. Although, PD theory developed for discontinuities, it gives good results for structures which have no discontinuities. In this paper, displacement control of the isotropic plate under the effect of tensile and bending loading has been investigated by means of PD theory. A MATLAB code is generated to create PD bonds and corresponding surface correction factors. Using generated MATLAB code the geometry of the specimen is generated, and the code is implemented in Finite Element Software. The results obtained from non-local continuum theory are compared with the Finite Element Analysis results and analytical solution. The results show good agreement.

Keywords: Flexural loading, non-local continuum mechanics, Peridynamic theory, solid structures, tensile loading.

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Authors: Zulkiflee M., Robiah Y., Nur Azman Abu, Shahrin S.

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Malware is software which was invented and meant for doing harms on computers. Malware is becoming a significant threat in computer network nowadays. Malware attack is not just only involving financial lost but it can also cause fatal errors which may cost lives in some cases. As new Internet Protocol version 6 (IPv6) emerged, many people believe this protocol could solve most malware propagation issues due to its broader addressing scheme. As IPv6 is still new compares to native IPv4, some transition mechanisms have been introduced to promote smoother migration. Unfortunately, these transition mechanisms allow some malwares to propagate its attack from IPv4 to IPv6 network environment. In this paper, a proof of concept shall be presented in order to show that some existing IPv4 malware detection technique need to be improvised in order to detect malware attack in dual-stack network more efficiently. A testbed of dual-stack network environment has been deployed and some genuine malware have been released to observe their behaviors. The results between these different scenarios will be analyzed and discussed further in term of their behaviors and propagation methods. The results show that malware behave differently on IPv6 from the IPv4 network protocol on the dual-stack network environment. A new detection technique is called for in order to cater this problem in the near future.

Keywords: Dual-Stack, Malware, Worm, IPv6;IDS

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Authors: Ahmed S. Elamary

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Ultimate shear resistance (USR) of slender plate girders can be predicted theoretically using Cardiff theory or Höglund theory. This paper will be concerned with predicting the USR using Höglund theory and EC3. Two main factors can affect the USR, the panel width “b” and the web depth “d”, consequently, the panel aspect ratio (b/d) has to be identified by limits. In most of the previous study, there is no limit for panel aspect ratio indicated. In this paper theoretical analysis has been conducted to study the effect of (b/d) on the USR. The analysis based on ninety six test results of steel plate girders subjected to shear executed and collected by others. New formula proposed to predict the percentage of the distance between the plastic hinges form in the flanges “c” to panel width “b”. Conservative limits of (c/b) have been suggested to get a consistent value of USR.

Keywords: Ultimate shear resistance, Plate Girder, Höglund’s theory, EC3.

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Authors: Narendra Singh Yadav, R.P.Yadav

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Mobile ad hoc network is a collection of mobile nodes communicating through wireless channels without any existing network infrastructure or centralized administration. Because of the limited transmission range of wireless network interfaces, multiple "hops" may be needed to exchange data across the network. In order to facilitate communication within the network, a routing protocol is used to discover routes between nodes. The primary goal of such an ad hoc network routing protocol is correct and efficient route establishment between a pair of nodes so that messages may be delivered in a timely manner. Route construction should be done with a minimum of overhead and bandwidth consumption. This paper examines two routing protocols for mobile ad hoc networks– the Destination Sequenced Distance Vector (DSDV), the table- driven protocol and the Ad hoc On- Demand Distance Vector routing (AODV), an On –Demand protocol and evaluates both protocols based on packet delivery fraction, normalized routing load, average delay and throughput while varying number of nodes, speed and pause time.

Keywords: AODV, DSDV, MANET, relative performance

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Authors: Sanaz Gharehzadeh Shirazi, Subira Gharehzadeh Shirazi, Fariba Jafari

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The present article comprises a theoretical study of structures Performat radical (HCO3) with H2O molecule. We make use of ab initio quantum chemical methods. Unrestricted Hartee-Fock (UHF) with the basis set6-311+g(2df,2p) and density functional theory (B3LYP) with the basis set 6-311+g(2df,2p) and also we done atoms in molecules (AIM) theory for them. We have found four stable geometries the PerformatRadical(HCO3) with H2O.

Keywords: Hydrogen binding, Performat Radical, Water, Gaussian, Atoms in molecules (AIM) theory

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Authors: Danielle Reichel, Antoine Druilhe, Tuan Dang

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Traditional development of wireless sensor network mote is generally based on SoC1 platform. Such method of development faces three main drawbacks: lack of flexibility in terms of development due to low resource and rigid architecture of SoC; low capability of evolution and portability versus performance if specific micro-controller architecture features are used; and the rapid obsolescence of micro-controller comparing to the long lifetime of power plants or any industrial installations. To overcome these drawbacks, we have explored a new approach of development of wireless sensor network mote using a hybrid FPGA technology. The application of such approach is illustrated through the implementation of an innovative wireless sensor network protocol called OCARI.

Keywords: Hybrid FPGA, Embedded system, Mote, flexibility, durability, OCARI protocol, SoC, Wireless Sensor Network

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Authors: Tanapong Khomyat, Peerapong Uthansakul, Monthippa Uthansakul

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In this paper, we evaluate the performance of the Hybrid-MIMO Receiver Scheme (HMRS) in Cognitive Radio network (CR-network). We investigate the efficiency of the proposed scheme which the energy level and user number of primary user are varied according to the characteristic of CR-network. HMRS can allow users to transmit either Space-Time Block Code (STBC) or Spatial-Multiplexing (SM) streams simultaneously by using Successive Interference Cancellation (SIC) and Maximum Likelihood Detection (MLD). From simulation, the results indicate that the interference level effects to the performance of HMRS. Moreover, the exact closed-form capacity of the proposed scheme is derived and compared with STBC scheme.

Keywords: Hybrid-MIMO, Cognitive radio network (CRnetwork), Symbol Error Rate (SER), Successive interference cancellation (SIC), Maximum likelihood detection (MLD).

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Authors: Christian Endisch, Christoph Hackl, Dierk Schröder

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This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg- Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach.

Keywords: System identification, dynamic neural network, recurrentneural network, GDNN, optimization, Levenberg Marquardt, realtime recurrent learning, network pruning, quasi-online learning.

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Authors: Yongyoon Suh, Chulhyun Kim, Moon-soo Kim

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As increasing importance of symbiosis and cooperation among mobile communication industries, the mobile ecosystem has been especially highlighted in academia and practice. The structure of mobile ecosystem is quite complex and the ecological role of actors is important to understand that structure. In this respect, this study aims to explore structure of mobile ecosystem in the case of Korea using inter-industry network analysis. Then, the ecological roles in mobile ecosystem are identified using centrality measures as a result of network analysis: degree of centrality, closeness, and betweenness. The result shows that the manufacturing and service industries are separate. Also, the ecological roles of some actors are identified based on the characteristics of ecological terms: keystone, niche, and dominator. Based on the result of this paper, we expect that the policy makers can formulate the future of mobile industry and healthier mobile ecosystem can be constructed.

Keywords: Mobile ecosystem, structure, ecological roles, network analysis, network index.

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Authors: Péter Restás, Andrea Czibor, Zsolt Péter Szabó

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Purpose: This article aims to rethink the phenomena of employee behavior as a product of a system. Both organizational culture and Complex Adaptive Systems (CAS) theory emphasize that individual behavior depends on the specific system and the unique organizational culture. These two major theories are both represented in the field of organizational studies; however, they are rarely used together for the comprehensive understanding of workplace behavior. Methodology: By reviewing the literature we use key concepts stemming from organizational culture and CAS theory in order to show the similarities between these theories and create an enriched understanding of employee behavior. Findings: a) Workplace behavior is defined here as social cognition issue. b) Organizations are discussed here as complex systems, and cultures which drive and dictate the cognitive processes of agents in the system. c) Culture gives CAS theory a context which lets us see organizations not just as ever-changing and unpredictable, but as such systems that aim to create and maintain stability by recurring behavior. Conclusion: Applying the knowledge from culture and CAS theory sheds light on our present understanding of employee behavior, also emphasizes the importance of novel ways in organizational research and management.

Keywords: Complex adaptive systems theory, employee behavior, organizational culture, stability.

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: Fault prediction, Neural network, GM (1.5), Genetic algorithm, GBPGA.

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Authors: M. Pollar, M. Jaroensutasinee, K. Jaroensutasinee

Abstract:

The population structure of the Tor tambroides was investigated with morphometric data (i.e. morphormetric measurement and truss measurement). A morphometric analysis was conducted to compare specimens from three waterfalls: Sunanta, Nan Chong Fa and Wang Muang waterfalls at Khao Nan National Park, Nakhon Si Thammarat, Southern Thailand. The results of stepwise discriminant analysis on seven morphometric variables and 21 truss variables per individual were the same as from a neural network. Fish from three waterfalls were separated into three groups based on their morphometric measurements. The morphometric data shows that the nerual network model performed better than the stepwise discriminant analysis.

Keywords: Morphometric, Tor tambroides, Stepwise Discriminant Analysis , Neural Network Analysis.

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Authors: Fereidoun Nowshiravan Rahatabad, Mohammad Ali Nekoui, Mohammad Reza Hashemi Golpaygani, AliFallah, Mehdi Kazemzadeh Narbat

Abstract:

The people are differed by their capabilities, skills and mental agilities. The evolution of human from childhood when they are completely dependent up to adultness the time they gradually set the dependency free is too complicated, by considering they have all started from almost one point but some become cleverer and some less. The main control command of a cybernetic hand should be posted by remaining healthy organs of disabled Person. These commands can be from several channels, which their recording and detecting are different and need complicated study. In this research, we suppose that, this stage has been done or in the other words, the command has been already sent and detected. So the main goal is to control a long hand, upper elbow hand missing, by an interest angle define by disabled. It means that, the system input is the position desired by disables and the output is the elbow-joint angle variation. Therefore the goal is a suitable control design based on neural network theory in order to meet the given mapping.

Keywords: Control - system design, Upper limb prosthesis, neuralnetwork.

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Authors: Earleen Jane Fuentes, Regeene Melarese Lim, Franklin Benjamin Tapia, Alexis Pantola

Abstract:

Mobile Ad-hoc Network (MANET) is an infrastructure-less network of mobile devices, also known as nodes. These nodes heavily rely on each other’s resources such as memory, computing power, and energy. Thus, some nodes may become selective in forwarding packets so as to conserve their resources. These nodes are called misleading nodes. Several reputation-based techniques (e.g. CORE, CONFIDANT, LARS, SORI, OCEAN) and acknowledgment-based techniques (e.g. TWOACK, S-TWOACK, EAACK) have been proposed to detect such nodes. These techniques do not appropriately punish misleading nodes. Hence, this paper addresses the limitations of these techniques using a system called MINDRA.

Keywords: Mobile ad-hoc network, selfish nodes, reputation-based techniques, acknowledgment-based techniques.

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Authors: Mangesh R. Phate, V. H. Tatwawadi

Abstract:

This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

Keywords: Field data based model, Artificial neural network, Simulation, Convectional Turning, Material removal rate.

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Authors: L. Ngowi, N. H. Mvungi

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This paper critically examines the evolution of socio-technical systems theory, its practices, and challenges in system design and development. It examines concepts put forward by researchers focusing on the application of the theory in software engineering. There are various methods developed that use socio-technical concepts based on systems engineering without remarkable success. The main constraint is the large amount of data and inefficient techniques used in the application of the concepts in system engineering for developing time-bound systems and within a limited/controlled budget. This paper critically examines each of the methods, highlight bottlenecks and suggest the way forward. Since socio-technical systems theory only explains what to do, but not how doing it, hence engineers are not using the concept to save time, costs and reduce risks associated with new frameworks. Hence, a new framework, which can be considered as a practical approach is proposed that borrows concepts from soft systems method, agile systems development and object-oriented analysis and design to bridge the gap between theory and practice. The approach will enable the development of systems using socio-technical systems theory to attract/enable the system engineers/software developers to use socio-technical systems theory in building worthwhile information systems to avoid fragilities and hostilities in the work environment.

Keywords: Socio-technical systems, human centered design, software engineering, cognitive engineering, soft systems, systems engineering.

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Authors: Garima Gupta, Daya Gupta

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Virtually all existing networked system management tools use a Manager/Agent paradigm. That is, distributed agents are deployed on managed devices to collect local information and report it back to some management unit. Even those that use standard protocols such as SNMP fall into this model. Using standard protocol has the advantage of interoperability among devices from different vendors. However, it may not be able to provide customized information that is of interest to satisfy specific management needs. In this dissertation work, different approaches are used to collect information regarding the devices attached to a Local Area Network. An SNMP aware application is being developed that will manage the discovery procedure and will be used as data collector.

Keywords: ICMP Scanner, Network Discovery, NetworkManagement, SNMP Scanner.

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Authors: Masaki Ito, Hideyuki Tokuda, Takao Kawamura, Kazunori Sugahara

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With the advancement of wireless sensor network technology, its practical utilization is becoming an important challange. This paper overviews my past environmental monitoring project, and discusses the process of starting the monitoring by classifying it into four steps. The steps to start environmental monitoring can be complicated, but not well discussed by researchers of wireless sensor network technology. This paper demonstrates our activity and challenges in each of the four steps to ease the process, and argues future challenges to enable quick start of environmental monitoring.

Keywords: Environmental Monitoring, Wireless Sensor Network, Field Experiment and Research Challenges.

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Authors: N. Bhalaji, A. R. Sivaramkrishnan, Sinchan Banerjee, V. Sundar, A. Shanmugam

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Nodes in mobile Ad Hoc Network (MANET) do not rely on a central infrastructure but relay packets originated by other nodes. Mobile ad hoc networks can work properly only if the participating nodes collaborate in routing and forwarding. For individual nodes it might be advantageous not to collaborate, though. In this conceptual paper we propose a new approach based on relationship among the nodes which makes them to cooperate in an Adhoc environment. The trust unit is used to calculate the trust values of each node in the network. The calculated trust values are being used by the relationship estimator to determine the relationship status of nodes. The proposed enhanced protocol was compared with the standard DSR protocol and the results are analyzed using the network simulator-2.

Keywords: Reliable Routing, DSR, Grudger, Adhoc network.

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Authors: D.O. Omondi, M.K. Walingo, G.M. Mbagaya, L.O.A. Othuon

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Many studies have applied the Theory of Planned Behavior (TPB) in predicting health behaviors among unique populations. However, a new paradigm is emerging where focus is now directed to modification and expansion of the TPB model rather than utilization of the traditional theory. This review proposes new models modified from the Theory of Planned Behavior and suggest an appropriate study design that can be used to test the models within physical activity and dietary practice domains among Type 2 diabetics in Kenya. The review was conducted by means of literature search in the field of nutrition behavior, health psychology and mixed methods using predetermined key words. The results identify pre-intention and post intention gaps within the TPB model that need to be filled. Additional psychosocial factors are proposed to be included in the TPB model to generate new models and the efficacy of these models tested using mixed methods design.

Keywords: Physical activity, diet, Type 2 diabetes, behaviorchange theory, model.

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Authors: S. Anna Durai, E. Anna Saro

Abstract:

Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly.

Keywords: Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.

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