Search results for: network security.

Authors: Narasimham Challa, Jayaram Pradhan

Abstract:

Cryptographic algorithms play a crucial role in the information society by providing protection from unauthorized access to sensitive data. It is clear that information technology will become increasingly pervasive, Hence we can expect the emergence of ubiquitous or pervasive computing, ambient intelligence. These new environments and applications will present new security challenges, and there is no doubt that cryptographic algorithms and protocols will form a part of the solution. The efficiency of a public key cryptosystem is mainly measured in computational overheads, key size and bandwidth. In particular the RSA algorithm is used in many applications for providing the security. Although the security of RSA is beyond doubt, the evolution in computing power has caused a growth in the necessary key length. The fact that most chips on smart cards can-t process key extending 1024 bit shows that there is need for alternative. NTRU is such an alternative and it is a collection of mathematical algorithm based on manipulating lists of very small integers and polynomials. This allows NTRU to high speeds with the use of minimal computing power. NTRU (Nth degree Truncated Polynomial Ring Unit) is the first secure public key cryptosystem not based on factorization or discrete logarithm problem. This means that given sufficient computational resources and time, an adversary, should not be able to break the key. The multi-party communication and requirement of optimal resource utilization necessitated the need for the present day demand of applications that need security enforcement technique .and can be enhanced with high-end computing. This has promoted us to develop high-performance NTRU schemes using approaches such as the use of high-end computing hardware. Peer-to-peer (P2P) or enterprise grids are proven as one of the approaches for developing high-end computing systems. By utilizing them one can improve the performance of NTRU through parallel execution. In this paper we propose and develop an application for NTRU using enterprise grid middleware called Alchemi. An analysis and comparison of its performance for various text files is presented.

Keywords: Alchemi, GridNtru, Ntru, PKCS.

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Authors: A. Safari, R. Jahani, H. A. Shayanfar, J. Olamaei

Abstract:

This paper shows the results obtained in the analysis of the impact of distributed generation (DG) on distribution losses and presents a new algorithm to the optimal allocation of distributed generation resources in distribution networks. The optimization is based on a Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO) aiming to optimal DG allocation in distribution network. Through this algorithm a significant improvement in the optimization goal is achieved. With a numerical example the superiority of the proposed algorithm is demonstrated in comparison with the simple genetic algorithm.

Keywords: Distributed Generation, Distribution Networks, Genetic Algorithm, Particle Swarm Optimization.

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Authors: Emad Alenany, M. Adel El-Baz

Abstract:

In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.

Keywords: Queueing network, discrete-event simulation, health applications, SPT.

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Authors: Shamsuddin Ahmed, M. G. M. Khan, Biman Prasad, Avlin Prasad

Abstract:

The focus of this paper is to construct daily time series exchange rate forecast models of Samoan Tala/USD and Tala/AUD during the year 2008 to 2012 with neural network The performance of the models was measured by using varies error functions such as Root Square mean error (RSME), Mean absolute error (MAE), and Mean absolute percentage error (MAPE). Our empirical findings suggest that AR (1) model is an effective tool to forecast the Tala/USD and Tala/AUD.

Keywords: Neural Network Forecasting Model, Autoregressive time series, Exchange rate, Tala/AUD, winters model.

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Authors: Yang Wenjuan, Xu Yilun

Abstract:

Based on the comparative analysis of the relevant evaluation methods of walking environment, this paper selects the combined evaluation method of macro urban morphology analysis and micro urban design quality survey, then investigates and analyzes the walking environment of three rail transit station area in Nanjing to explore the influence factor and internal relation of walkability of rail transit station area. Analysis shows that micro urban design factors have greater impact on the walkability of rail transit station area compared with macro urban morphology factors, the convenience is the key factor in the four aspects of convenience, security, identity and comfortability of the urban design factors, the convenience is not only affected by the block network form, but also related to the quality of the street space. The overall evaluation of walkability comes from the overlapping and regrouping of the walking environment at different levels, but some environmental factors play a leading role. The social attributes of pedestrians also partly influence their walking perception and evaluation.

Keywords: Rail transit station area, walkability, evaluation, influence factors.

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Authors: Nurazean Maarop, Noorjan Mohd Mustapha, Rasimah Yusoff, Roslina Ibrahim, Norziha Megat Mohd Zainuddin

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The goal of this study is to identify success factors that could influence the ISMS self-implementation in government sector from qualitative perspective. This study is based on a case study in one of the Malaysian government agency. Semi-structured interviews involving five key informants were conducted to examine factors addressed in the conceptual framework. Subsequently, thematic analysis was executed to describe the influence of each factor on the success implementation of ISMS. The result of this study indicates that management commitment, implementer commitment and implementer competency are part of the success factors for ISMS self-implementation in Malaysian Government Sector.

Keywords: ISMS Success Factors, IT Project Management, IS Success, Information Security.

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Authors: M. R. Mustafa, M. H. Isa, R. B. Rezaur

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The use of artificial neural network (ANN) modeling for prediction and forecasting variables in water resources engineering are being increasing rapidly. Infrastructural applications of ANN in terms of selection of inputs, architecture of networks, training algorithms, and selection of training parameters in different types of neural networks used in water resources engineering have been reported. ANN modeling conducted for water resources engineering variables (river sediment and discharge) published in high impact journals since 2002 to 2011 have been examined and presented in this review. ANN is a vigorous technique to develop immense relationship between the input and output variables, and able to extract complex behavior between the water resources variables such as river sediment and discharge. It can produce robust prediction results for many of the water resources engineering problems by appropriate learning from a set of examples. It is important to have a good understanding of the input and output variables from a statistical analysis of the data before network modeling, which can facilitate to design an efficient network. An appropriate training based ANN model is able to adopt the physical understanding between the variables and may generate more effective results than conventional prediction techniques.

Keywords: ANN, discharge, modeling, prediction, sediment,

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Authors: Victor Krebss

Abstract:

Task of object localization is one of the major challenges in creating intelligent transportation. Unfortunately, in densely built-up urban areas, localization based on GPS only produces a large error, or simply becomes impossible. New opportunities arise for the localization due to the rapidly emerging concept of a wireless ad-hoc network. Such network, allows estimating potential distance between these objects measuring received signal level and construct a graph of distances in which nodes are the localization objects, and edges - estimates of the distances between pairs of nodes. Due to the known coordinates of individual nodes (anchors), it is possible to determine the location of all (or part) of the remaining nodes of the graph. Moreover, road map, available in digital format can provide localization routines with valuable additional information to narrow node location search. However, despite abundance of well-known algorithms for solving the problem of localization and significant research efforts, there are still many issues that currently are addressed only partially. In this paper, we propose localization approach based on the graph mapped distances on the digital road map data basis. In fact, problem is reduced to distance graph embedding into the graph representing area geo location data. It makes possible to localize objects, in some cases even if only one reference point is available. We propose simple embedding algorithm and sample implementation as spatial queries over sensor network data stored in spatial database, allowing employing effectively spatial indexing, optimized spatial search routines and geometry functions.

Keywords: Intelligent Transportation System, Sensor Network, Localization, Spatial Query, GIS, Graph Embedding.

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Authors: R. R. Chen, K. Khorasani

Abstract:

In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.

Keywords: Congestion control, Large scale networks, Decentralized control, Differentiated services traffic, Time-delay systems.

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Authors: Raquel M. de Sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

Abstract:

Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of back propagation of back propagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this caseiodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: Artificial Neural Networks, Biodiesel, Iodine Value, Prediction.

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Authors: Tarifa S. Almulhim, Ludmil Mikhailov, Dong-Ling Xu

Abstract:

In this paper, a method for deriving a group priority vector in the Fuzzy Analytic Network Process (FANP) is proposed. By introducing importance weights of multiple decision makers (DMs) based on their experiences, the Fuzzy Preferences Programming Method (FPP) is extended to a fuzzy group prioritization problem in the FANP. Additionally, fuzzy pair-wise comparison judgments are presented rather than exact numerical assessments in order to model the uncertainty and imprecision in the DMs- judgments and then transform the fuzzy group prioritization problem into a fuzzy non-linear programming optimization problem which maximize the group satisfaction. Unlike the known fuzzy prioritization techniques, the new method proposed in this paper can easily derive crisp weights from incomplete and inconsistency fuzzy set of comparison judgments and does not require additional aggregation producers. Detailed numerical examples are used to illustrate the implement of our approach and compare with the latest fuzzy prioritization method.

Keywords: Fuzzy Analytic Network Process (FANP), Fuzzy Non-linear Programming, Fuzzy Preferences Programming Method (FPP), Multiple Criteria Decision-Making (MCDM), Triangular Fuzzy Number.

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Authors: Y. Harold Robinson, M. Rajaram, E. Golden Julie, S. Balaji

Abstract:

Mobile ad hoc network (MANET) is a self-configuring network of mobile node connected without wires. A Fuzzy Logic Based Collaborative watchdog approach is used to reduce the detection time of misbehaved nodes and increase the overall truthfulness. This methodology will increase the secure efficient routing by detecting the Black Holes attacks. The simulation results proved that this method improved the energy, reduced the delay and also improved the overall performance of the detecting black hole attacks in MANET.

Keywords: MANET, collaborative watchdog, fuzzy logic, AODV.

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Authors: Hock Chuan, Lim

Abstract:

This paper presents an approach for identifying interactive effects using Network Science (NS) supported by Social Network Analysis (SNA) techniques. Based on general observations that learning processes and behaviors are shaped by the social relationships and influenced by learning environment, the central idea was to understand both the human and non-human interactive effects for a blended learning mode of delivery of computer science modules. Important findings include (a) the importance of non-human nodes to influence the centrality and transfer; (b) the degree of non-human and human connectivity impacts learning. This project reveals that the NS pattern and connectivity as measured by node relationships offer alternative approach for hypothesis generation and design of qualitative data collection. An iterative process further reinforces the analysis, whereas the experimental simulation option itself is an interesting alternative option, a hybrid combination of both experimental simulation and qualitative data collection presents itself as a promising and viable means to study complex scenario such as blended learning delivery mode. The primary value of this paper lies in the design of the approach for studying interactive effects of human (social nodes) and non-human (learning/study environment, Information and Communication Technologies (ICT) infrastructures nodes) components. In conclusion, this project adds to the understanding and the use of SNA to model and study interactive effects in blended social learning.

Keywords: Blended learning, network science, social learning, social network analysis, study environment.

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Authors: Taiwo. M. Akinmuyisitan, John Cosmas

Abstract:

This paper practically demonstrates the theories and concepts of an Advanced Convolutional Neural Network in the design and development of a scalable artificial intelligence model for the detection of criminal masterminds. The technique uses machine vision algorithms to compute the facial characteristics of suspects and classify actors as criminal or non-criminal faces. The paper proceeds further to compare the results of the error accuracy of two popular custom convolutional pre-trained networks, VGG16 and Resnet50. The result shows that VGG16 is probably more efficient than ResNet50 for the dataset we used.

Keywords: Artificial intelligence, convolutional neural networks, Resnet50, VGG16.

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Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

Abstract:

Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: Satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization.

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

Abstract:

A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: Behavioural biometric, Face biometric, Neural network, Physical biometric, Signature biometric.

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Authors: H. Izadbakhsh, M.Hour Ali, A. Amirkhani, A. Montazeri, M. Saberi

Abstract:

The after–sales activities are nowadays acknowledged as a relevant source of revenue, profit and competitive advantage in most manufacturing industries. Top and middle management, therefore, should focus on the definition of a structured business performance measurement system for the after-sales business. The paper aims at filling this gap, and presents an integrated methodology for the after-sales network performance measurement, and provides an empirical application to automotive case companies and their official service network. This is the first study that presents an integrated multivariate approach for total assessment and improvement of after-sale services.

Keywords: Data Envelopment Analysis (DEA), Principal Component Analysis (PCA), Automotive companies, After-sale services.

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Authors: Hui Tag, Jambey Tsering, Pallabi Kalita Hui, Baikuntha Jyoti Gogoi, Vijay Veer

Abstract:

The food security issues and its relevance in High Mountain regions of the world have been often neglected. Wild edible plants have been playing a major role in livelihood security among the tribal Communities of East Himalayan Region of the world since time immemorial. The Eastern Himalayan Region of India is one of the mega diverse regions of world and rated as top 12th Global Biodiversity Hotspots by IUCN and recognized as one of the 200 significant eco-regions of the Globe. The region supports one of the world’s richest alpine floras and about one-third of them are endemic to the region. There are at least 7,500 flowering plants, 700 orchids, 58 bamboo species, 64 citrus species, 28 conifers, 500 mosses, 700 ferns and 728 lichens. The region is the home of more than three hundred different ethnic communities having diverse knowledge on traditional uses of flora and fauna as food, medicine and beverages. Monpa, Memba and Khamba are among the local communities residing in high altitude region of Eastern Himalaya with rich traditional knowledge related to utilization of wild edible plants. The Monpas, Memba and Khamba are the followers Mahayana sect of Himalayan Buddhism and they are mostly agrarian by primary occupation and also heavily relaying on wild edible plants for their livelihood security during famine since millennia. In the present study, we have reported traditional uses of 40 wild edible plant species and out of which 6 species were analyzed at biochemical level for nutrients contents and free radical scavenging activities. The results have shown significant free radical scavenging (antioxidant) activity and nutritional potential of the selected 6 wild edible plants used by the local communities of Eastern Himalayan Region of India.

Keywords: East Himalaya, Local community, Wild edible plants, Nutrition, Food security.

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Authors: Muhammad I. Ibrahimy, Mamun B.I. Reaz, Khandaker Asaduzzaman, Sazzad Hussain

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An approach to develop the FPGA of a flexible key RSA encryption engine that can be used as a standard device in the secured communication system is presented. The VHDL modeling of this RSA encryption engine has the unique characteristics of supporting multiple key sizes, thus can easily be fit into the systems that require different levels of security. A simple nested loop addition and subtraction have been used in order to implement the RSA operation. This has made the processing time faster and used comparatively smaller amount of space in the FPGA. The hardware design is targeted on Altera STRATIX II device and determined that the flexible key RSA encryption engine can be best suited in the device named EP2S30F484C3. The RSA encryption implementation has made use of 13,779 units of logic elements and achieved a clock frequency of 17.77MHz. It has been verified that this RSA encryption engine can perform 32-bit, 256-bit and 1024-bit encryption operation in less than 41.585us, 531.515us and 790.61us respectively.

Keywords: RSA, FPGA, Communication, Security, VHDL.

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Authors: Kazunari Meguro, Shinichi Motomura, Takao Kawamura, Kazunori Sugahara

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We have developed a distributed asynchronous Web based training system. In order to improve the scalability and robustness of this system, all contents and a function are realized on mobile agents. These agents are distributed to computers, and they can use a Peer to Peer network that modified Content-Addressable Network. In this system, all computers offer the function and exercise by themselves. However, the system that all computers do the same behavior is not realistic. In this paper, as a solution of this issue, we present an e-Learning system that is composed of computers of different participation types. Enabling the computer of different participation types will improve the convenience of the system.

Keywords: Distributed Multimedia Systems, e-Learning, P2P, Mobile Agen

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Authors: Shunsuke Saito, Kazuho Yoshimoto, Shunichi Ohmori, Sirawadee Arunyanart

Abstract:

In this paper, we suggest a mechanism of assessment that rater and Ratee (or employees) to convince. There are many problems exist in the personnel assessment. In particular, we were focusing on the three. (1) Raters are not sufficiently recognized assessment point. (2) Ratee are not convinced by the mechanism of assessment. (3) Raters (or Evaluators) and ratees have empathy. We suggest 1: Setting of "understanding of the assessment points." 2: Setting of "relative assessment ability." 3: Proposal of two-way assessment mechanism to solve these problems. As a prerequisite, it is assumed that there are multiple raters. This is because has been a growing importance of multi-faceted assessment. In this model, it determines the weight of each assessment point evaluators by the degree of understanding and assessment ability of raters and ratee. We used the ANP (Analytic Network Process) is a theory that an extension of the decision-making technique AHP (Analytic Hierarchy Process). ANP can be to address the problem of forming a network and assessment of Two-Way is possible. We apply this technique personnel assessment, the weights of rater of each point can be reasonably determined. We suggest absolute assessment for Two-Way assessment by ANP. We have verified that the consent of the two approaches is higher than conventional mechanism. Also, human resources consultant we got a comment about the application of the practice.

Keywords: Personnel assessment, ANP (analytic network process), two-way.

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Authors: Shafaf Ibrahim, Noor Elaiza Abdul Khalid, Mazani Manaf

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Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper compares the performances of Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Controlled experimental data is used, which designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various sizes of abnormalities and pasting it onto normal brain tissues. The normal tissues or the background are divided into three different categories. The segmentation is done with fifty seven data of each category. The knowledge of the size of the abnormalities by the number of pixels are then compared with segmentation results of three techniques proposed. It was proven that the ANFIS returns the best segmentation performances in light abnormalities, whereas the SBRG on the other hand performed well in dark abnormalities segmentation.

Keywords: Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS), Fuzzy c-Means (FCM), Brain segmentation.

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Authors: Mei Shan Ngan, Chee Wei Tan

Abstract:

Due to the non-linear characteristics of photovoltaic (PV) array, PV systems typically are equipped with the capability of maximum power point tracking (MPPT) feature. Moreover, in the case of PV array under partially shaded conditions, hotspot problem will occur which could damage the PV cells. Partial shading causes multiple peaks in the P-V characteristic curves. This paper presents a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) MPPT algorithm for the detection of global peak among the multiple peaks in order to extract the true maximum energy from PV panel. The PV system consists of PV array, dc-dc boost converter controlled by the proposed MPPT algorithm and a resistive load. The system was simulated using MATLAB/Simulink package. The simulation results show that the proposed algorithm performs well to detect the true global peak power. The results of the simulations are analyzed and discussed.

Keywords: Photovoltaic (PV), Partial Shading, Maximum Power Point Tracking (MPPT), Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)

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Authors: Masoud Hassanzade Novin, Bahloul Salmani

Abstract:

The process of the translation is not merely the linguistic aspects. It is also considered in the cultural framework of both the source and target text cultures. The translation process and translated texts are confronted the new aspect in 20th century which is considered mostly in the patronage framework and ideological grillwork of the target language. To have these factors scrutinized in the process of the translation both micro-element factors and macro-element factors can be taken into consideration. For the purpose of this study through a qualitative type of research based on critical discourse analysis approach, the case study of the novel “1984” written by George Orwell was chosen as the corpus of the study to have the contrastive analysis by its Persian translated texts. Results of the study revealed some distortions embedded in the target texts which were overshadowed by ideological aspect and patronage network. The outcomes of the manipulated terms were different in various categories which revealed the manipulation aspects in the texts translated.

Keywords: Critical discourse analysis, ideology, translated texts, patronage network.

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: Big data, k-NN, machine learning, traffic speed prediction.

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Authors: Kavi K. Khedo, R. K. Subramanian

Abstract:

Wireless sensor networks (WSN) are currently receiving significant attention due to their unlimited potential. These networks are used for various applications, such as habitat monitoring, automation, agriculture, and security. The efficient nodeenergy utilization is one of important performance factors in wireless sensor networks because sensor nodes operate with limited battery power. In this paper, we proposed the MiSense hierarchical cluster based routing algorithm (MiCRA) to extend the lifetime of sensor networks and to maintain a balanced energy consumption of nodes. MiCRA is an extension of the HEED algorithm with two levels of cluster heads. The performance of the proposed protocol has been examined and evaluated through a simulation study. The simulation results clearly show that MiCRA has a better performance in terms of lifetime than HEED. Indeed, MiCRA our proposed protocol can effectively extend the network lifetime without other critical overheads and performance degradation. It has been noted that there is about 35% of energy saving for MiCRA during the clustering process and 65% energy savings during the routing process compared to the HEED algorithm.

Keywords: Clustering algorithm, energy consumption, hierarchical model, sensor networks.

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Authors: Wann-Ming Wey

Abstract:

In today's popularity and progress of information technology, the big data set and its analysis are no longer a major conundrum. Now, we could not only use the relevant big data to analysis and emulate the possible status of urban development in the near future, but also provide more comprehensive and reasonable policy implementation basis for government units or decision-makers via the analysis and emulation results as mentioned above. In this research, we set Taipei City as the research scope, and use the relevant big data variables (e.g., population, facility utilization and related social policy ratings) and Analytic Network Process (ANP) approach to implement in-depth research and discussion for the possible reduction of land use in primary and secondary schools of Taipei City. In addition to enhance the prosperous urban activities for the urban public facility utilization, the final results of this research could help improve the efficiency of urban land use in the future. Furthermore, the assessment model and research framework established in this research also provide a good reference for schools or other public facilities land use and adaptive reuse strategies in the future.

Keywords: Adaptive reuse, analytic network process, big data, land use strategy.

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Authors: Sharil Tumin, Sylvia Encheva

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Centrally controlled authentication and authorization services can provide enterprise with an increase in security, more flexible access control solutions and an increased users' trust. By using redirections, users of all Web-based applications within an organization are authenticated at a single well known and secure Web site and using secure communication protocol. Users are first authenticated at the central server using their domain wide credentials before being redirected to a particular Web-based application. The central authentication server will then provide others with pertinence authorization related particulars and credentials of the authenticated user to the specific application. The trust between the clients and the server hosts is established by secure session keys exchange. Case- studies are provided to demonstrate the usefulness and flexibility of the proposed solution.

Keywords: Authentication, Authorization, Security, Protected Web-based Applications

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Authors: Danladi Ali, Onah Festus Iloabuchi

Abstract:

In this paper, GSM signal strength was measured in order to detect the type of the signal fading phenomenon using onedimensional multilevel wavelet residual method and neural network clustering to determine the average GSM signal strength received in the study area. The wavelet residual method predicted that the GSM signal experienced slow fading and attenuated with MSE of 3.875dB. The neural network clustering revealed that mostly -75dB, -85dB and -95dB were received. This means that the signal strength received in the study is a weak signal.

Keywords: One-dimensional multilevel wavelets, path loss, GSM signal strength, propagation and urban environment.

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Authors: V.Manikandan, N.Devarajan

Abstract:

The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.

Keywords: Artificial neural network, Fault Diagnosis, Identification, Markov parameters.

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