Search results for: network identification

Authors: Win Thandar Soe, Innocent Mpawenimana, Mathieu Di Fazio, Cécile Belleudy, Aung Ze Ya

Abstract:

Intelligent electronic equipment and automation network is the brain of high-tech energy management systems in critical role of smart homes dominance. Smart home is a technology integration for greater comfort, autonomy, reduced cost, and energy saving as well. These services can be provided to home owners for managing their home appliances locally or remotely and consequently allow them to automate intelligently and responsibly their consumption by individual or collective control systems. In this study, three smart plugs are described and one of them tested on typical household appliances. This article proposes to collect the data from the wireless technology and to extract some smart data for energy management system. This smart data is to quantify for three kinds of load: intermittent load, phantom load and continuous load. Phantom load is a waste power that is one of unnoticed power of each appliance while connected or disconnected to the main. Intermittent load and continuous load take in to consideration the power and using time of home appliances. By analysing the classification of loads, this smart data will be provided to reduce the communication of wireless sensor network for energy management system.

Keywords: Energy management, load profile, smart plug, wireless sensor network.

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Authors: Hayder Natiq Jasem, Zuriati Ahmad Zukarnain, Mohamed Othman, Shamala Subramaniam

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Congestion control is one of the fundamental issues in computer networks. Without proper congestion control mechanisms there is the possibility of inefficient utilization of resources, ultimately leading to network collapse. Hence congestion control is an effort to adapt the performance of a network to changes in the traffic load without adversely affecting users perceived utilities. AIMD (Additive Increase Multiplicative Decrease) is the best algorithm among the set of liner algorithms because it reflects good efficiency as well as good fairness. Our control model is based on the assumption of the original AIMD algorithm; we show that both efficiency and fairness of AIMD can be improved. We call our approach is New AIMD. We present experimental results with TCP that match the expectation of our theoretical analysis.

Keywords: Congestion control, Efficiency, Fairness, TCP, AIMD.

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Authors: Yasin Tadayonrad, Alassane Ballé Ndiaye

Abstract:

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

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

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Authors: Jun-Ho Park, Kwang-Woo Nam, Seung-Mo Hong, Tae-Heon Moon, Sang-Ho Lee, Youn-Taik Leem

Abstract:

The aim of this study was to build ‘Ubi-Net’, a decision-making support system for systematic establishment in U-City planning. We have experienced various urban problems caused by high-density development and population concentrations in established urban areas. To address these problems, a U-Service contributes to the alleviation of urban problems by providing real-time information to citizens through network connections and related information. However, technology, devices, and information for consumers are required for systematic U-Service planning in towns and cities where there are many difficulties in this regard, and a lack of reference systems. Thus, this study suggests methods to support the establishment of sustainable planning by providing comprehensive information including IT technology, devices, news, and social networking services (SNS) to U-City planners through intelligent searches. In this study, we targeted Smart U-Parking Planning to solve parking problems in an ‘old’ city. Through this study, we sought to contribute to supporting advances in U-Space and the alleviation of urban problems.

Keywords: Design and decision support system, smart U-parking planning, social network analysis.

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Authors: Tan Soo Fun, Leau Yu Beng, Rozaini Roslan, Habeeb Saleh Habeeb

Abstract:

The increasing development of wireless networks and the widespread popularity of handheld devices such as Personal Digital Assistants (PDAs), mobile phones and wireless tablets represents an incredible opportunity to enable mobile devices as a universal payment method, involving daily financial transactions. Unfortunately, some issues hampering the widespread acceptance of mobile payment such as accountability properties, privacy protection, limitation of wireless network and mobile device. Recently, many public-key cryptography based mobile payment protocol have been proposed. However, limited capabilities of mobile devices and wireless networks make these protocols are unsuitable for mobile network. Moreover, these protocols were designed to preserve traditional flow of payment data, which is vulnerable to attack and increase the user-s risk. In this paper, we propose a private mobile payment protocol which based on client centric model and by employing symmetric key operations. The proposed mobile payment protocol not only minimizes the computational operations and communication passes between the engaging parties, but also achieves a completely privacy protection for the payer. The future work will concentrate on improving the verification solution to support mobile user authentication and authorization for mobile payment transactions.

Keywords: Mobile Network Operator, Mobile payment protocol, Privacy, Symmetric key.

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Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, dissimilarity

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Authors: Jeong-Tae Kim, Tchanghee Hyun

Abstract:

The number of intrusions and attacks against critical infrastructures and other information networks is increasing rapidly. While there is no identified evidence that terrorist organizations are currently planning a coordinated attack against the vulnerabilities of computer systems and network connected to critical infrastructure, and origins of the indiscriminate cyber attacks that infect computers on network remain largely unknown. The growing trend toward the use of more automated and menacing attack tools has also overwhelmed some of the current methodologies used for tracking cyber attacks. There is an ample possibility that this kind of cyber attacks can be transform to cyberterrorism caused by illegal purposes. Cyberterrorism is a matter of vital importance to national welfare. Therefore, each countries and organizations have to take a proper measure to meet the situation and consider effective legislation about cyberterrorism.

Keywords: Cyberterrorism, cyber attack, information security, legislation

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Authors: Rachid.Dehini, Abdesselam.Bassou, Brahim.Ferdi

Abstract:

Active Power Filters (APFs) are today the most widely used systems to eliminate harmonics compensate power factor and correct unbalanced problems in industrial power plants. We propose to improve the performances of conventional APFs by using artificial neural networks (ANNs) for harmonics estimation. This new method combines both the strategies for extracting the three-phase reference currents for active power filters and DC link voltage control method. The ANNs learning capabilities to adaptively choose the power system parameters for both to compute the reference currents and to recharge the capacitor value requested by VDC voltage in order to ensure suitable transit of powers to supply the inverter. To investigate the performance of this identification method, the study has been accomplished using simulation with the MATLAB Simulink Power System Toolbox. The simulation study results of the new (SAPF) identification technique compared to other similar methods are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion.

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Authors: Parma Nand, S.C. Sharma

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The Mobile Ad-hoc Network (MANET) is a collection of self-configuring and rapidly deployed mobile nodes (routers) without any central infrastructure. Routing is one of the potential issues. Many routing protocols are reported but it is difficult to decide which one is best in all scenarios. In this paper on demand routing protocols DSR and DYMO based on IEEE 802.11 DCF MAC protocol are examined and characteristic summary of these routing protocols is presented. Their performance is analyzed and compared on performance measuring metrics throughput, dropped packets due to non availability of routes, duplicate RREQ generated for route discovery and normalized routing load by varying CBR data traffic load using QualNet 5.0.2 network simulator.

Keywords: Adhoc networks, wireless networks, CBR, routingprotocols, route discovery, simulation, performance evaluation, MAC, IEEE 802.11.

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Authors: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith

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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.

Keywords: Clustering, heterogeneous, stability, scalability, throughput, IoT, WSN.

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Authors: Shih-Yan Chen, Shin-Shin Kao

Abstract:

In this paper, a thorough review about dual-cubes, DCn, the related studies and their variations are given. DCn was introduced to be a network which retains the pleasing properties of hypercube Qn but has a much smaller diameter. In fact, it is so constructed that the number of vertices of DCn is equal to the number of vertices of Q2n +1. However, each vertex in DCn is adjacent to n + 1 neighbors and so DCn has (n + 1) × 2^2n edges in total, which is roughly half the number of edges of Q2n+1. In addition, the diameter of any DCn is 2n +2, which is of the same order of that of Q2n+1. For selfcompleteness, basic definitions, construction rules and symbols are provided. We chronicle the results, where eleven significant theorems are presented, and include some open problems at the end.

Keywords: Hypercubes, dual-cubes, fault-tolerant hamiltonian property, dual-cube extensive networks, dual-cube-like networks.

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Authors: Giovanni Luca Masala

Abstract:

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Keywords: Computer Aided Detection, mammary tumor, pattern recognition, thalassemia.

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Authors: Vidhu Mitha Goutham, Ruma Mukherjee

Abstract:

The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: Attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation.

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

Abstract:

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

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

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Authors: Souleymane Oumtanaga, Prosper Kimou, Kouadio Gaza Kevin

Abstract:

The security of their network remains the priorities of almost all companies. Existing security systems have shown their limit; thus a new type of security systems was born: honeypots. Honeypots are defined as programs or intended servers which have to attract pirates to study theirs behaviours. It is in this context that the leurre.com project of gathering about twenty platforms was born. This article aims to specify a model of honeypots attack. Our model describes, on a given platform, the evolution of attacks according to theirs hours. Afterward, we show the most attacked services by the studies of attacks on the various ports. It is advisable to note that this article was elaborated within the framework of the research projects on honeyspots within the LABTIC (Laboratory of Information Technologies and Communication).

Keywords: Honeypot, networks, attack, leurrecom, computer network

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Authors: Imen Aouini, Lamia Ben Azzouz

Abstract:

The energy need is growing rapidly due to the population growth and the large new usage of power. Several works put considerable efforts to make the electricity grid more intelligent to reduce essentially energy consumption and provide efficiency and reliability of power systems. The Smart Grid is a complex architecture that covers critical devices and systems vulnerable to significant attacks. Hence, security is a crucial factor for the success and the wide deployment of Smart Grids. In this paper, we present security issues of the Smart Grid architecture and we highlight open issues that will make the Smart Grid security a challenging research area in the future.

Keywords: Smart grids, smart meters, home area network, neighbor area network.

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

Abstract:

Cosmic showers, from their places of origin in space, after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of EAS and similar High Energy Particle Showers involve a plethora of experimental setups with certain constraints for which soft-computational tools like Artificial Neural Network (ANN)s can be adopted. The optimality of ANN classifiers can be enhanced further by the use of Multiple Classifier System (MCS) and certain data - dimension reduction techniques. This work describes the performance of certain data dimension reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Self Organizing Map (SOM) approximators for application with an MCS formed using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN). The data inputs are obtained from an array of detectors placed in a circular arrangement resembling a practical detector grid which have a higher dimension and greater correlation among themselves. The PCA, ICA and SOM blocks reduce the correlation and generate a form suitable for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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Authors: T. Alghamdi, G. Alaghband

Abstract:

In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution neural network, edges, face recognition, support vector machine.

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Authors: Michael K. Adu, Boniface K. Alese, Olumide S. Ogunnusi

Abstract:

This paper brings to fore the inherent advantages in application of mobile agents to procure software products rather than downloading software content on the Internet. It proposes a system whereby the products come on compact disk with mobile agent as deliverable. The client/user purchases a software product, but must connect to the remote server of the software developer before installation. The user provides an activation code that activates mobile agent which is part of the software product on compact disk. The validity of the activation code is checked on connection at the developer’s end to ascertain authenticity and prevent piracy. The system is implemented by downloading two different software products as compare with installing same products on compact disk with mobile agent’s application. Downloading software contents from developer’s database as in the traditional method requires a continuously open connection between the client and the developer’s end, a fixed network is not economically or technically feasible. Mobile agent after being dispatched into the network becomes independent of the creating process and can operate asynchronously and autonomously. It can reconnect later after completing its task and return for result delivery. Response Time and Network Load are very minimal with application of Mobile agent.

Keywords: Activation code, internet, mobile agent, software developer, software products.

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Authors: Ε. Giovanis

Abstract:

In this paper we apply an Adaptive Network-Based Fuzzy Inference System (ANFIS) with one input, the dependent variable with one lag, for the forecasting of four macroeconomic variables of US economy, the Gross Domestic Product, the inflation rate, six monthly treasury bills interest rates and unemployment rate. We compare the forecasting performance of ANFIS with those of the widely used linear autoregressive and nonlinear smoothing transition autoregressive (STAR) models. The results are greatly in favour of ANFIS indicating that is an effective tool for macroeconomic forecasting used in academic research and in research and application by the governmental and other institutions

Keywords: Linear models, Macroeconomics, Neuro-Fuzzy, Non-Linear models

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Authors: Shengbiao Wu, Weihua Cao, Min Wu, Can Liu

Abstract:

This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision.

Keywords: Microwave filter, scattering parameter (s-parameter), coupling matrix, intelligent tuning.

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Authors: Huan Chen, Bo-Chao Cheng, Chih-Chuan Cheng, Yi-Geng Chen, Yu Ling Chou

Abstract:

Wireless Sensor Network (WSN) comprises of sensor nodes which are designed to sense the environment, transmit sensed data back to the base station via multi-hop routing to reconstruct physical phenomena. Since physical phenomena exists significant overlaps between temporal redundancy and spatial redundancy, it is necessary to use Redundancy Suppression Algorithms (RSA) for sensor node to lower energy consumption by reducing the transmission of redundancy. A conventional algorithm of RSAs is threshold-based RSA, which sets threshold to suppress redundant data. Although many temporal and spatial RSAs are proposed, temporal-spatial RSA are seldom to be proposed because it is difficult to determine when to utilize temporal or spatial RSAs. In this paper, we proposed a novel temporal-spatial redundancy suppression algorithm, Codebookbase Redundancy Suppression Mechanism (CRSM). CRSM adopts vector quantization to generate a codebook, which is easily used to implement temporal-spatial RSA. CRSM not only achieves power saving and reliability for WSN, but also provides the predictability of network lifetime. Simulation result shows that the network lifetime of CRSM outperforms at least 23% of that of other RSAs.

Keywords: Redundancy Suppression Algorithm (RSA), Threshold-based RSA, Temporal RSA, Spatial RSA and Codebookbase Redundancy Suppression Mechanism (CRSM)

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Authors: Hanaa T. El-Madany, Faten H. Fahmy, Ninet M. A. El-Rahman, Hassen T. Dorrah

Abstract:

Advancements in the field of artificial intelligence (AI) made during this decade have forever changed the way we look at automating spacecraft subsystems including the electrical power system. AI have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper, a mathematical modeling and MATLAB–SIMULINK model for the different components of the spacecraft power system is presented. Also, a control system, which includes either the Neural Network Controller (NNC) or the Fuzzy Logic Controller (FLC) is developed for achieving the coordination between the components of spacecraft power system as well as control the energy flows. The performance of the spacecraft power system is evaluated by comparing two control systems using the NNC and the FLC.

Keywords: Spacecraft, Neural network, Fuzzy logic control, Photovoltaic array.

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Authors: M.K. Bhuyan, Aragala Jagan.

Abstract:

Current image-based individual human recognition methods, such as fingerprints, face, or iris biometric modalities generally require a cooperative subject, views from certain aspects, and physical contact or close proximity. These methods cannot reliably recognize non-cooperating individuals at a distance in the real world under changing environmental conditions. Gait, which concerns recognizing individuals by the way they walk, is a relatively new biometric without these disadvantages. The inherent gait characteristic of an individual makes it irreplaceable and useful in visual surveillance. In this paper, an efficient gait recognition system for human identification by extracting two features namely width vector of the binary silhouette and the MPEG-7-based region-based shape descriptors is proposed. In the proposed method, foreground objects i.e., human and other moving objects are extracted by estimating background information by a Gaussian Mixture Model (GMM) and subsequently, median filtering operation is performed for removing noises in the background subtracted image. A moving target classification algorithm is used to separate human being (i.e., pedestrian) from other foreground objects (viz., vehicles). Shape and boundary information is used in the moving target classification algorithm. Subsequently, width vector of the outer contour of binary silhouette and the MPEG-7 Angular Radial Transform coefficients are taken as the feature vector. Next, the Principal Component Analysis (PCA) is applied to the selected feature vector to reduce its dimensionality. These extracted feature vectors are used to train an Hidden Markov Model (HMM) for identification of some individuals. The proposed system is evaluated using some gait sequences and the experimental results show the efficacy of the proposed algorithm.

Keywords: Gait Recognition, Gaussian Mixture Model, PrincipalComponent Analysis, MPEG-7 Angular Radial Transform.

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Authors: Eklas Hossain, Sheroz Khan, Ahad Ali

Abstract:

Power line communications may be used as a data communication channel in public and indoor distribution networks so that it does not require the installing of new cables. Industrial low voltage distribution network may be utilized for data transfer required by the on-line condition monitoring of electric motors. This paper presents a pilot distribution network for modeling low voltage power line as data transfer channel. The signal attenuation in communication channels in the pilot environment is presented and the analysis is done by varying the corresponding parameters for the signal attenuation.

Keywords: Data communication, indoor distribution networks, low voltage, power line.

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Authors: Agus Harjoko, Sri Hartati, Henry Dwiyasa

Abstract:

There have been numerous implementations of security system using biometric, especially for identification and verification cases. An example of pattern used in biometric is the iris pattern in human eye. The iris pattern is considered unique for each person. The use of iris pattern poses problems in encoding the human iris. In this research, an efficient iris recognition method is proposed. In the proposed method the iris segmentation is based on the observation that the pupil has lower intensity than the iris, and the iris has lower intensity than the sclera. By detecting the boundary between the pupil and the iris and the boundary between the iris and the sclera, the iris area can be separated from pupil and sclera. A step is taken to reduce the effect of eyelashes and specular reflection of pupil. Then the four levels Coiflet wavelet transform is applied to the extracted iris image. The modified Hamming distance is employed to measure the similarity between two irises. This research yields the identification success rate of 84.25% for the CASIA version 1.0 database. The method gives an accuracy of 77.78% for the left eyes of MMU 1 database and 86.67% for the right eyes. The time required for the encoding process, from the segmentation until the iris code is generated, is 0.7096 seconds. These results show that the accuracy and speed of the method is better than many other methods.

Keywords: Biometric, iris recognition, wavelet transform.

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Authors: Mohd Fairuz Iskandar Othman, Nazrulazhar Bahaman, Zulkiflee Muslim, Faizal Abdollah

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This paper discusses a curriculum approach that will give emphasis on practical portions of teaching network security subjects in information and communication technology courses. As we are well aware, the need to use a practice and application oriented approach in education is paramount. Research on active learning and cooperative groups have shown that students grasps more and have more tendency towards obtaining and realizing soft skills like leadership, communication and team work as opposed to the more traditional theory and exam based teaching and learning. While this teaching and learning paradigm is relatively new in Malaysia, it has been practiced widely in the West. This paper examines a certain approach whereby students learning wireless security are divided into and work in small and manageable groups where there will be 2 teams which consist of black hat and white hat teams. The former will try to find and expose vulnerabilities in a wireless network while the latter will try their best to prevent such attacks on their wireless networks using hardware, software, design and enforcement of security policy and etc. This paper will try to show that the approach taken plus the use of relevant and up to date software and hardware and with suitable environment setting will hopefully expose students to a more fruitful outcome in terms of understanding of concepts, theories and their motivation to learn.

Keywords: Curriculum approach, wireless networks, wirelesssecurity.

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Authors: Travis Wiens, Greg Schoenau, Rich Burton

Abstract:

It is well known that a linear dynamic system including a delay will exhibit limit cycle oscillations when a bang-bang sensor is used in the feedback loop of a PID controller. A similar behaviour occurs when a delayed feedback signal is used to train a neural network. This paper develops a method of predicting this behaviour by linearizing the system, which can be shown to behave in a manner similar to an integral controller. Using this procedure, it is possible to predict the characteristics of the neural network driven limit cycle to varying degrees of accuracy, depending on the information known about the system. An application is also presented: the intelligent control of a spark ignition engine.

Keywords: Control and automation, artificial neural networks, limit cycle

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Authors: Htay Htay Yi, Zin May Aye

Abstract:

Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: False negative rate, intrusion detection system, machine learning methods, performance.

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Authors: Eylem Koç, Hasan Arda Burhan

Abstract:

Location selection presents a crucial decision problem in today’s business world where strategic decision making processes have critical importance. Thus, location selection has strategic importance for companies in boosting their strength regarding competition, increasing corporate performances and efficiency in addition to lowering production and transportation costs. A right choice in location selection has a direct impact on companies’ commercial success. In this study, a store location selection problem of Carglass Turkey which operates in vehicle glass branch is handled. As this problem includes both tangible and intangible criteria, Analytic Network Process (ANP) was accepted as the main methodology. The model consists of control hierarchy and BOCR subnetworks which include clusters of actors, alternatives and criteria. In accordance with the management’s choices, five different locations were selected. In addition to the literature review, a strict cooperation with the actor group was ensured and maintained while determining the criteria and during whole process. Obtained results were presented to the management as a report and its feasibility was confirmed accordingly.

Keywords: Analytic Network Process, BOCR, location selection, multi-actor decision making, multi-criteria decision making, real life problem.

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