Search results for: sampling algorithms

Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy.

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Authors: Vijay Kumar Nath, Deepika Hazarika, Anil Mahanta,

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Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.

Keywords: Lapped orthogonal transform, Lapped biorthogonal transform, Image compression, KS test,

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Authors: Phatthanan Chaiyabut

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The purposes of this research were to identify the perception of customers towards Krung Thai Bank’s image and to understand the customer attitude towards Krung Thai Bank’s image in Bangkok, Thailand. This research utilized quantitative approach and used questionnaire as data collection tool. A sample size of 420 respondents was selected by simple random sampling. The findings revealed that the majority of respondents received information, news, and feeds concerning the bank through televisions the most. This information channel had significantly influenced on the customers and their decisions to utilize the bank’s products and services.

From the information concerning the attitudes towards overall image of the bank, it was found that the majority respondents rated the bank’s image at the good level. The top three average attitudes included the bank’s images in supports government's monetary policies, being renowned and stable, and contributing in economical amendments and developments, with the mean average of 4.01, 3.96 and 3.81 respectively. The attitudes toward the images included a business leader in banking, marketing, and competitions. Offering prompt services, and provided appropriate servicing time were rated moderate with the attitudes of 3.36 and 3.30 respectively.

Keywords: Attitude, Image, Krung Thai bank, Perception.

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Authors: Kimutai Joan Jepkorir, Moturi Wilkister Nyaora

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The practice of open defecation can be devastating for human health as well as the environment, and this practice persistence could be due to ingrained habits that individuals continue to engage in despite having a better alternative. Safe disposal of human excreta is essential for public health protection. This study sought to find if open defecation relates to fecal-oral infections in Burat and Ngaremara Wards in Isiolo County. This was achieved through conducting a cross-sectional study. Simple random sampling technique was used to select 385 households that were used in the study. Data collection was done by use of questionnaires and observation checklists. The result show that 66% of the respondents disposed-off fecal matter in a safe manner, whereas 34% disposed-off fecal matter in unsafe manner through open defecation. The prevalence proportions per 1000 of diarrhea and intestinal worms among children under-5 years of age were 142 and 21, respectively. The prevalence proportions per 1000 of diarrhea and typhoid among children over-5 years of age were 20 and 20, respectively.

Keywords: Fecal-oral infections, open defecation, prevalence proportion, sanitation.

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.

Keywords: Clustering, k-means, categorical datasets, pattern recognition, unsupervised learning, knowledge discovery.

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Authors: S. Panda, S. C. Swain, A. K. Baliarsingh, A. K. Mohanty, C. Ardil

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Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.

Keywords: Multi-objective optimisation, thyristor controlled series compensator, power system stability, genetic algorithm, pareto solution set, fuzzy ranking.

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Authors: S. Sowmyayani, P. Arockia Jansi Rani

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This paper integrates Octagon and Square Search pattern (OCTSS) motion estimation algorithm into H.264/AVC (Advanced Video Coding) video codec in Adaptive Group of Pictures (AGOP) mode. AGOP structure is computed based on scene change in the video sequence. Octagon and square search pattern block-based motion estimation method is implemented in inter-prediction process of H.264/AVC. Both these methods reduce bit rate and computational complexity while maintaining the quality of the video sequence respectively. Experiments are conducted for different types of video sequence. The results substantially proved that the bit rate, computation time and PSNR gain achieved by the proposed method is better than the existing H.264/AVC with fixed GOP and AGOP. With a marginal gain in quality of 0.28dB and average gain in bitrate of 132.87kbps, the proposed method reduces the average computation time by 27.31 minutes when compared to the existing state-of-art H.264/AVC video codec.

Keywords: Block Distortion Measure, Block Matching Algorithms, H.264/AVC, Motion estimation, Search patterns, Shot cut detection.

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Authors: Safaa Al-Ali, Ahmad Shahin, Fadi Chakik

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Image coding based on clustering provides immediate access to targeted features of interest in a high quality decoded image. This approach is useful for intelligent devices, as well as for multimedia content-based description standards. The result of image clustering cannot be precise in some positions especially on pixels with edge information which produce ambiguity among the clusters. Even with a good enhancement operator based on PDE, the quality of the decoded image will highly depend on the clustering process. In this paper, we introduce an ambiguity cluster in image coding to represent pixels with vagueness properties. The presence of such cluster allows preserving some details inherent to edges as well for uncertain pixels. It will also be very useful during the decoding phase in which an anisotropic diffusion operator, such as Perona-Malik, enhances the quality of the restored image. This work also offers a comparative study to demonstrate the effectiveness of a fuzzy clustering technique in detecting the ambiguity cluster without losing lot of the essential image information. Several experiments have been carried out to demonstrate the usefulness of ambiguity concept in image compression. The coding results and the performance of the proposed algorithms are discussed in terms of the peak signal-tonoise ratio and the quantity of ambiguous pixels.

Keywords: Ambiguity Cluster, Anisotropic Diffusion, Fuzzy Clustering, Image Compression.

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Authors: Ludovic Favre, Thibaut M. Schafer, Jean-Luc Robyr, Elena-Lavinia Niederhäuser

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This paper presents an optimization method based on genetic algorithm for the energy management inside buildings developed in the frame of the project Smart Living Lab (SLL) in Fribourg (Switzerland). This algorithm optimizes the interaction between renewable energy production, storage systems and energy consumers. In comparison with standard algorithms, the innovative aspect of this project is the extension of the smart regulation over three simultaneous criteria: the energy self-consumption, the decrease of greenhouse gas emissions and operating costs. The genetic algorithm approach was chosen due to the large quantity of optimization variables and the non-linearity of the optimization function. The optimization process includes also real time data of the building as well as weather forecast and users habits. This information is used by a physical model of the building energy resources to predict the future energy production and needs, to select the best energetic strategy, to combine production or storage of energy in order to guarantee the demand of electrical and thermal energy. The principle of operation of the algorithm as well as typical output example of the algorithm is presented.

Keywords: Building’s energy, control system, energy management, modelling, genetic optimization algorithm, renewable energy, greenhouse gases, energy storage.

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Authors: Joy Jo-Yi Chang, Justie Su-Tzu Juan

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Visual secret sharing (VSS) was proposed by Naor and Shamir in 1995. Visual secret sharing schemes encode a secret image into two or more share images, and single share image can’t obtain any information about the secret image. When superimposes the shares, it can restore the secret by human vision. Due to the traditional VSS have some problems like pixel expansion and the cost of sophisticated. And this method only can encode one secret image. The schemes of encrypting more secret images by random grids into two shares were proposed by Chen et al. in 2008. But when those restored secret images have much distortion, those schemes are almost limited in decoding. In the other words, if there is too much distortion, we can’t encrypt too much information. So, if we can adjust distortion to very small, we can encrypt more secret images. In this paper, four new algorithms which based on Chang et al.’s scheme be held in 2010 are proposed. First algorithm can adjust distortion to very small. Second algorithm distributes the distortion into two restored secret images. Third algorithm achieves no distortion for special secret images. Fourth algorithm encrypts three secret images, which not only retain the advantage of VSS but also improve on the problems of decoding.

Keywords: Visual cryptography, visual secret sharing, random grids, multiple, secret image sharing

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Authors: Yananda Siraphatthada

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This policy participation action research explores the roles of Thai government units during its 2010 fiscal year on how to create value added to recycling business in the central part of Thailand. The research aims to a) study how the government plays a role to support the business, and its problems and obstacles on supporting the business, b) to design a strategic action – short, medium, and long term plans -- to create value added to the recycling business, particularly in local full-loop companies/organizations licensed by Wongpanit Waste Separation Plant as well as those licensed by the Department of Provincial Administration. Mixed method research design, i.e., a combination of quantitative and qualitative methods is utilized in the present study in both data collection and analysis procedures. Quantitative data was analyzed by frequency, percent value, mean scores, and standard deviation, and aimed to note trend and generalizations. Qualitative data was collected via semi-structured interviews/focus group interviews to explore in-depth views of the operators. The sampling included 1,079 operators in eight provinces in the central part of Thailand.

Keywords: Management, Recycling Business, Value Added.

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Authors: Kongrit Jittangthammagul, Sakesun Yampinij, Thapanee Endoo, Nattapong Kramwong

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The study entitled “The Construction of Interactive Computer Multimedia Instruction on Basic Japanese Vocabulary" was aimed: 1) To construct the interactive computer multimedia instruction on Basic Japanese Vocabulary, 2) To find out multimedia-s quality, 3) To examine the student-s satisfaction and 4) To study the learning achievement in Basic Japanese vocabulary. The sampling group used in this study was composed of 40 1st year student in Educational Communications and Technology Department, Faculty of Industrial Education and Technology, King Mongkut-s University of Technology Thonburi, in the academic year 2553 B.E. (2010). According to research results, we found that 1). The quality assessment by 3 mass media experts was at 4.72 on average or at high level. 2) In terms of contents, the evaluation by 3 experts was at 4.81 on average or at high level. 3) In terms of achievement, there was a statistical significance between before and after the treatment at the .05 level. 4) The satisfaction of students towards the interactive computer multimedia Instruction on “Basic Japanese Vocabulary" was 4.35 on average, or at high level.

Keywords: Interactive Computer Multimedia on Basic Japanese Vocabulary, Learning Achievement, Quality

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Authors: Tripatjot S. Panag, J. S. Dhillon

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The lifetime of a wireless sensor network can be effectively increased by using scheduling operations. Once the sensors are randomly deployed, the task at hand is to find the largest number of disjoint sets of sensors such that every sensor set provides complete coverage of the target area. At any instant, only one of these disjoint sets is switched on, while all other are switched off. This paper proposes a heuristic search method to find the maximum number of disjoint sets that completely cover the region. A population of randomly initialized members is made to explore the solution space. A set of heuristics has been applied to guide the members to a possible solution in their neighborhood. The heuristics escalate the convergence of the algorithm. The best solution explored by the population is recorded and is continuously updated. The proposed algorithm has been tested for applications which require sensing of multiple target points, referred to as point coverage applications. Results show that the proposed algorithm outclasses the existing algorithms. It always finds the optimum solution, and that too by making fewer number of fitness function evaluations than the existing approaches.

Keywords: Coverage, disjoint sets, heuristic, lifetime, scheduling, wireless sensor networks, WSN.

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Authors: Deniz T. Sodiri, Venkat V S S Sastry

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The paper presents an on-line recognition machine (RM) for continuous/isolated, dynamic and static gestures that arise in Flight Deck Officer (FDO) training. RM is based on generic pattern recognition framework. Gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of gestures via dynamic programming and Markovian process. The algorithm predicts corresponding index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. The algorithm provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. In the present paper, we consider isolated gestures. The performance of RM is evaluated using four datasets - artificial (W TTest), hand motion (Yang) and FDO (tracker, vision-based ). RM achieves comparable results which are in agreement with other on-line and off-line algorithms such as hidden Markov model (HMM) and dynamic time warping (DTW). The proposed algorithm has the additional advantage of providing timely feedback for training purposes.

Keywords: On-line Recognition Algorithm, IsolatedDynamic/Static Gesture Recognition, On-line Markovian/DynamicProgramming, Training in Virtual Environments.

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Authors: Philomena Ibuh Adzongo, Margaret U. Oluwole, Justina Nguveren Jor.

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This paper investigated the need for the development of entrepreneurial skills for Benue State University students. The population consisted of all 1,500 final year students in Benue State University. A sample of 100 students was selected using simple random sampling. A 12-item self-constructed and content validated questionnaire by research experts titled, the Need for the Development of Entrepreneurial Skills in Benue State University Students (NDECBSUS) was used to collect the data. The questionnaire items were rated using a 4-point modified rating scale of Strongly Agree, Agree, Disagree and Strongly Disagree, assigned the following scores of 4,3,2 and 1, respectively. The questionnaire was administered by the researcher with the help of two research assistants through the primary source. Simple percentages and chi-square were used to answer the research questions and test the hypotheses, respectively. The findings revealed that in business management, business management skills, personal skills, and technical skills need to be developed in students for them to become effective and efficient entrepreneurs and concluded that the acquisition of these skills will reduce the challenge of unemployment. The study recommended that funds should be made available by all education stakeholders for such programmes to remain functional.

Keywords: Entrepreneurial skill, entrepreneurship, need for development, university students.

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Authors: Seyed Ziya Tabatabaei, Azimi Bin Hj Hamzah, Fatemeh Ebrahimi

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The increase in proportion of older people in Malaysia has led to a significant growth of health care demands. The aim of this study is to explore how perceived health care needs influence on quality of life among elderly Malay residents who reside in a Malaysian residential home. This study employed a method known as ethnographic research from May 2011 to January 2012. Four data collection strategies were selected as the main data-collecting tools including participant observation, field notes, in-depth interviews, and review of related documents. The nine knowledgeable participants for the present study were selected using the purposive sampling method. Two themes were identified: (1) Medical concerns: Feeling secure, lack of information, inadequate medical staff; and (2) Health promotion: Body condition, health education, physiotherapy and rehabilitation. These results could evoke the attention of policy-makers and care providers to better meet elderly residents’ health care needs.

Keywords: Ethnographic study, health care needs, elderly Malay people, Malaysia, quality of life, residential home.

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Authors: Paola Lecca, Lorenzo Dematte, Corrado Priami

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The present models and simulation algorithms of intracellular stochastic kinetics are usually based on the premise that diffusion is so fast that the concentrations of all the involved species are homogeneous in space. However, recents experimental measurements of intracellular diffusion constants indicate that the assumption of a homogeneous well-stirred cytosol is not necessarily valid even for small prokaryotic cells. In this work a mathematical treatment of diffusion that can be incorporated in a stochastic algorithm simulating the dynamics of a reaction-diffusion system is presented. The movement of a molecule A from a region i to a region j of the space is represented as a first order reaction Ai k- ! Aj , where the rate constant k depends on the diffusion coefficient. The diffusion coefficients are modeled as function of the local concentration of the solutes, their intrinsic viscosities, their frictional coefficients and the temperature of the system. The stochastic time evolution of the system is given by the occurrence of diffusion events and chemical reaction events. At each time step an event (reaction or diffusion) is selected from a probability distribution of waiting times determined by the intrinsic reaction kinetics and diffusion dynamics. To demonstrate the method the simulation results of the reaction-diffusion system of chaperoneassisted protein folding in cytoplasm are shown.

Keywords: Reaction-diffusion systems, diffusion coefficient, stochastic simulation algorithm.

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Authors: K. Vijayalakshmi, S. Radhakrishnan

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In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.

Keywords: Dynamic Group membership change, Hybrid Genetic Algorithm, Link / node failure, QoS Parameters.

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Authors: Xiaoming Jiang, Jinqiao Shi, Qingfeng Tan, Wentao Zhang, Xuebin Wang, Muqian Chen

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Nowadays, big companies such as Google, Microsoft, which have adequate proxy servers, have perfectly implemented their web crawlers for a certain website in parallel. But due to lack of expensive proxy servers, it is still a puzzle for researchers to crawl large amounts of information from a single website in parallel. In this case, it is a good choice for researchers to use free public proxy servers which are crawled from the Internet. In order to improve efficiency of web crawler, the following two issues should be considered primarily: (1) Tasks may fail owing to the instability of free proxy servers; (2) A proxy server will be blocked if it visits a single website frequently. In this paper, we propose Proxisch, an optimization approach of large-scale unstable proxy servers scheduling, which allow anyone with extremely low cost to run a web crawler efficiently. Proxisch is designed to work efficiently by making maximum use of reliable proxy servers. To solve second problem, it establishes a frequency control mechanism which can ensure the visiting frequency of any chosen proxy server below the website’s limit. The results show that our approach performs better than the other scheduling algorithms.

Keywords: Proxy server, priority queue, optimization approach, distributed web crawling.

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Authors: Norazah Mohd Suki, Norbayah Mohd Suki

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Online shopping enables consumers to search for information and purchase products or services through direct interaction with online store. This study aims to examine the effect of Internet marketing environment, product characteristics, familiarity and confidence, and promotional offers on consumer online shopping behavior. 200 questionnaires were distributed to the respondents, who are students and staff at a public university in the Federal Territory of Labuan, Malaysia, following simple random sampling as a means of data collection. Multiple regression analysis was used as a statistical measure to determine the strength of the relationship between one dependent variable and a series of other independent variables. Results revealed that familiarity and confidence was found to greatly influence consumer online shopping behavior followed by promotional offers. A clear understanding of consumer online shopping behavior can help marketing managers predict the online shopping rate and evaluate the future growth of online commerce.

Keywords: Internet Marketing Environment, Product Characteristics, Multiple Regression Analysis, Malaysia.

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Authors: Diogen Babuc

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The question that is a motivation of writing is how many devote themselves to discovering something in the world of science where much is discerned and revealed, but at the same time, much is unknown. The insightful elements of this algorithm are the ciphering and deciphering algorithms of Playfair, Caesar, and Vigen`ere. Only a few of their main properties are taken and modified, with the aim of forming a specific functionality of the algorithm called Latin Djokovic. Specifically, a string is entered as input data. A key k is given, with a random value between the values a and b = a+3. The obtained value is stored in a variable with the aim of being constant during the run of the algorithm. In correlation to the given key, the string is divided into several groups of substrings, and each substring has a length of k characters. The next step involves encoding each substring from the list of existing substrings. Encoding is performed using the basis of Caesar algorithm, i.e. shifting with k characters. However, that k is incremented by 1 when moving to the next substring in that list. When the value of k becomes greater than b + 1, it will return to its initial value. The algorithm is executed, following the same procedure, until the last substring in the list is traversed. Using this polyalphabetic method, ciphering and deciphering of strings are achieved. The algorithm also works for a 100-character string. The x character is not used when the number of characters in a substring is incompatible with the expected length. The algorithm is simple to implement, but it is questionable if it works better than the other methods, from the point of view of execution time and storage space.

Keywords: Ciphering and deciphering, Authentic Algorithm, Polyalphabetic Cipher, Random Key, methods comparison.

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Authors: Dilusha Weeraddana, Ashanie Gunathillake, Samiru Gayan

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In an emergency, combining Wireless Sensor Network's data with the knowledge gathered from various other information sources and navigation algorithms, could help safely guide people to a building exit while avoiding the risky areas. This paper presents an emergency response and navigation support architecture for data gathering, knowledge manipulation, and navigational support in an emergency situation. At normal state, the system monitors the environment. When an emergency event detects, the system sends messages to first responders and immediately identifies the risky areas from safe areas to establishing escape paths. The main functionalities of the system include, gathering data from a wireless sensor network which is deployed in a multi-story indoor environment, processing it with information available in a knowledge base, and sharing the decisions made, with first responders and people in the building. The proposed architecture will act to reduce risk of losing human lives by evacuating people much faster with least congestion in an emergency environment. 

Keywords: Emergency response, Firefighters, Navigation, Wireless sensor network.

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Authors: A.K.Balirsingh, S.C.Swain, S. Panda

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Recently, Genetic Algorithms (GA) and Differential Evolution (DE) algorithm technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of DE and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques has been compared. Further, the optimized controllers are tested on a weekly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.

Keywords: Differential Evolution, Flexible AC TransmissionSystems (FACTS), Genetic Algorithm, Low Frequency Oscillations, Single-machine Infinite Bus Power System.

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Authors: Mohamed Gouskir, Belaid Bouikhalene, Hicham Aissaoui, Benachir Elhadadi

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In this paper, we present an application of Riemannian geometry for processing non-Euclidean image data. We consider the image as residing in a Riemannian manifold, for developing a new method to brain edge detection and brain extraction. Automating this process is a challenge due to the high diversity in appearance brain tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based anisotropic diffusion tensor for the segmentation task by integrating both image edge geometry and Riemannian manifold (geodesic, metric tensor) to regularize the convergence contour and extract complex anatomical structures. We check the accuracy of the segmentation results on simulated brain MRI scans of single T1-weighted, T2-weighted and Proton Density sequences. We validate our approach using two different databases: BrainWeb database, and MRI Multiple sclerosis Database (MRI MS DB). We have compared, qualitatively and quantitatively, our approach with the well-known brain extraction algorithms. We show that using a Riemannian manifolds to medical image analysis improves the efficient results to brain extraction, in real time, outperforming the results of the standard techniques.

Keywords: Riemannian manifolds, Riemannian Tensor, Brain Segmentation, Non-Euclidean data, Brain Extraction.

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Authors: Shiuh-Jer Huang, Yu-Chi Liu, Su-Hai Hsiang

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A novel low-cost impedance control structure is proposed for monitoring the contact force between end-effector and environment without installing an expensive force/torque sensor. Theoretically, the end-effector contact force can be estimated from the superposition of each joint control torque. There have a nonlinear matrix mapping function between each joint motor control input and end-effector actuating force/torques vector. This new force control structure can be implemented based on this estimated mapping matrix. First, the robot end-effector is manipulated to specified positions, then the force controller is actuated based on the hall sensor current feedback of each joint motor. The model-free fuzzy sliding mode control (FSMC) strategy is employed to design the position and force controllers, respectively. All the hardware circuits and software control programs are designed on an Altera Nios II embedded development kit to constitute an embedded system structure for a retrofitted Mitsubishi 5 DOF robot. Experimental results show that PI and FSMC force control algorithms can achieve reasonable contact force monitoring objective based on this hardware control structure.

Keywords: Robot, impedance control, fuzzy sliding mode control, contact force estimator.

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

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This paper describes the proficient way of choosing the cluster head based on dominating set algorithm in a wireless sensor network (WSN). The algorithm overcomes the energy deterioration problems by this selection process of cluster heads. Clustering algorithms such as LEACH, EEHC and HEED enhance scalability in WSNs. Dominating set algorithm keeps the first node alive longer than the other protocols previously used. As the dominating set of cluster heads are directly connected to each node, the energy of the network is saved by eliminating the intermediate nodes in WSN. Security and trust is pivotal in network messaging. Cluster head is secured with a unique key. The member can only connect with the cluster head if and only if they are secured too. The secured trust model provides security for data transmission in the dominated set network with the group key. The concept can be extended to add a mobile sink for each or for no of clusters to transmit data or messages between cluster heads and to base station. Data security id preferably high and data loss can be prevented. The simulation demonstrates the concept of choosing cluster heads by dominating set algorithm and trust evaluation using DSTE. The research done is rationalized.

Keywords: Wireless Sensor Networks, LEECH, EEHC, HEED, DSTE.

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

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Average temperatures worldwide are expected to continue to rise. At the same time, major cities in developing countries are becoming increasingly populated and polluted. Governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of a model, which is able to estimate the occupant exposure to extreme temperatures and high air pollution within domestic buildings. Building physics simulations were performed using the EnergyPlus building physics software. An accurate metamodel is then formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) have been compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: Neural Networks, Radial Basis Functions, Metamodelling, Python machine learning libraries.

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Authors: Abdesselem Dakhli, Wajdi Bellil, Chokri Ben Amar

Abstract:

DNA Barcode provides good sources of needed information to classify living species. The classification problem has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use the similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. However, all the used methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. In fact, our method permits to avoid the complex problem of form and structure in different classes of organisms. The empirical data and their classification performances are compared with other methods. Evenly, in this study, we present our system which is consisted of three phases. The first one, is called transformation, is composed of three sub steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. Moreover, the second phase step is an approximation; it is empowered by the use of Multi Library Wavelet Neural Networks (MLWNN). Finally, the third one, is called the classification of DNA Barcodes, is realized by applying the algorithm of hierarchical classification.

Keywords: DNA Barcode, Electron-Ion Interaction Pseudopotential, Multi Library Wavelet Neural Networks.

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Authors: S. Umarani, D. Sharmila

Abstract:

A Distributed Denial of Service (DDoS) attack is a major threat to cyber security. It originates from the network layer or the application layer of compromised/attacker systems which are connected to the network. The impact of this attack ranges from the simple inconvenience to use a particular service to causing major failures at the targeted server. When there is heavy traffic flow to a target server, it is necessary to classify the legitimate access and attacks. In this paper, a novel method is proposed to detect DDoS attacks from the traces of traffic flow. An access matrix is created from the traces. As the access matrix is multi dimensional, Principle Component Analysis (PCA) is used to reduce the attributes used for detection. Two classifiers Naive Bayes and K-Nearest neighborhood are used to classify the traffic as normal or abnormal. The performance of the classifier with PCA selected attributes and actual attributes of access matrix is compared by the detection rate and False Positive Rate (FPR).

Keywords: Distributed Denial of Service (DDoS) attack, Application layer DDoS, DDoS Detection, K- Nearest neighborhood classifier, Naive Bayes Classifier, Principle Component Analysis.

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Authors: M. Gooroochurn, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs

Abstract:

This paper presents the automated methods employed for extracting craniofacial landmarks in white light images as part of a registration framework designed to support three neurosurgical procedures. The intraoperative space is characterised by white light stereo imaging while the preoperative plan is performed on CT scans. The registration aims at aligning these two modalities to provide a calibrated environment to enable image-guided solutions. The neurosurgical procedures can then be carried out by mapping the entry and target points from CT space onto the patient-s space. The registration basis adopted consists of natural landmarks (eye corner and ear tragus). A 5mm accuracy is deemed sufficient for these three procedures and the validity of the selected registration basis in achieving this accuracy has been assessed by simulation studies. The registration protocol is briefly described, followed by a presentation of the automated techniques developed for the extraction of the craniofacial features and results obtained from tests on the AR and FERET databases. Since the three targeted neurosurgical procedures are routinely used for head injury management, the effect of bruised/swollen faces on the automated algorithms is assessed. A user-interactive method is proposed to deal with such unpredictable circumstances.

Keywords: Face Processing, Craniofacial Feature Extraction, Preoperative to Intraoperative Registration, Registration Basis.

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