Phase Control Array Synthesis Using Constrained Accelerated Particle Swarm Optimization

In this paper, the phase control antenna array synthesis is presented. The problem is formulated as a constrained optimization problem that imposes nulls with prescribed level while maintaining the sidelobe at a prescribed level. For efficient use of the algorithm memory, compared to the well known Particle Swarm Optimization (PSO), the Accelerated Particle Swarm Optimization (APSO) is used to estimate the phase parameters of the synthesized array. The objective function is formed using a main objective and set of constraints with penalty factors that measure the violation of each feasible solution in the search space to each constraint. In this case the obtained feasible solution is guaranteed to satisfy all the constraints. Simulation results have shown significant performance increases and a decreased randomness in the parameter search space compared to a single objective conventional particle swarm optimization.

Towards Clustering of Web-based Document Structures

Methods for organizing web data into groups in order to analyze web-based hypertext data and facilitate data availability are very important in terms of the number of documents available online. Thereby, the task of clustering web-based document structures has many applications, e.g., improving information retrieval on the web, better understanding of user navigation behavior, improving web users requests servicing, and increasing web information accessibility. In this paper we investigate a new approach for clustering web-based hypertexts on the basis of their graph structures. The hypertexts will be represented as so called generalized trees which are more general than usual directed rooted trees, e.g., DOM-Trees. As a important preprocessing step we measure the structural similarity between the generalized trees on the basis of a similarity measure d. Then, we apply agglomerative clustering to the obtained similarity matrix in order to create clusters of hypertext graph patterns representing navigation structures. In the present paper we will run our approach on a data set of hypertext structures and obtain good results in Web Structure Mining. Furthermore we outline the application of our approach in Web Usage Mining as future work.

Hybrid Approach for Memory Analysis in Windows System

Random Access Memory (RAM) is an important device in computer system. It can represent the snapshot on how the computer has been used by the user. With the growth of its importance, the computer memory has been an issue that has been discussed in digital forensics. A number of tools have been developed to retrieve the information from the memory. However, most of the tools have their limitation in the ability of retrieving the important information from the computer memory. Hence, this paper is aimed to discuss the limitation and the setback for two main techniques such as process signature search and process enumeration. Then, a new hybrid approach will be presented to minimize the setback in both individual techniques. This new approach combines both techniques with the purpose to retrieve the information from the process block and other objects in the computer memory. Nevertheless, the basic theory in address translation for x86 platforms will be demonstrated in this paper.

A Study on Barreling Behavior during Upsetting Process using Artificial Neural Networks with Levenberg Algorithm

In this paper back-propagation artificial neural network (BPANN )with Levenberg–Marquardt algorithm is employed to predict the deformation of the upsetting process. To prepare a training set for BPANN, some finite element simulations were carried out. The input data for the artificial neural network are a set of parameters generated randomly (aspect ratio d/h, material properties, temperature and coefficient of friction). The output data are the coefficient of polynomial that fitted on barreling curves. Neural network was trained using barreling curves generated by finite element simulations of the upsetting and the corresponding material parameters. This technique was tested for three different specimens and can be successfully employed to predict the deformation of the upsetting process

Agreement Options on Multi Criteria Group Decision and Negotiation

This paper presents a conceptual model of agreement options on negotiation support for civil engineering decision. The negotiation support facilitates the solving of group choice decision making problems in civil engineering decision to reduce the impact of mud volcano disaster in Sidoarjo, Indonesia. The approach based on application of analytical hierarchy process (AHP) method for multi criteria decision on three level of decision hierarchy. Decisions for reducing impact is very complicated since many parties involved in a critical time. Where a number of stakeholders are involved in choosing a single alternative from a set of solution alternatives, there are different concern caused by differing stakeholder preferences, experiences, and background. Therefore, a group choice decision support is required to enable each stakeholder to evaluate and rank the solution alternatives before engaging into negotiation with the other stakeholders. Such civil engineering solutions as alternatives are referred to as agreement options that are determined by identifying the possible stakeholder choice, followed by determining the optimal solution for each group of stakeholder. Determination of the optimal solution is based on a game theory model of n-person general sum game with complete information that involves forming coalitions among stakeholders.

Quantification of Heart Rate Variability: A Measure based on Unique Heart Rates

It is established that the instantaneous heart rate (HR) of healthy humans keeps on changing. Analysis of heart rate variability (HRV) has become a popular non invasive tool for assessing the activities of autonomic nervous system. Depressed HRV has been found in several disorders, like diabetes mellitus (DM) and coronary artery disease, characterised by autonomic nervous dysfunction. A new technique, which searches for pattern repeatability in a time series, is proposed specifically for the analysis of heart rate data. These set of indices, which are termed as pattern repeatability measure and pattern repeatability ratio are compared with approximate entropy and sample entropy. In our analysis, based on the method developed, it is observed that heart rate variability is significantly different for DM patients, particularly for patients with diabetic foot ulcer.

A Study on the User Experience Design of Mobile Twitter Application

The number of people using SNS with their mobile devices is soaring. This research focuses on the Twitter service that has the most third-party applications and delved into the fact that there were not sufficient studies on the UX design aspects of Twitter applications. Among social network services which have emerged as a major social topic lately, this research try to analyze the UX design of the Twitter application which is also called micro-blogging service. Therefore this research sets its goal to draw components of the UX design aspect of the Tweeter application on which there are not enough analysis yet. Moreover, this research suggests improvement of mobile application which will assure better users- experience. In order to analyze the UX design aspect of the mobile twitter application, with relevant document and user research, evaluating factors of the UX Design which would affect users- experience were organized. The subjects for cases were selected among six paid and free social networking applications that had been consistently ranked from 1st to 100th in the Korean application store during May, 2012 after closely monitoring the rank. From May 15th to May 11th in 2012, in accordance with the evaluating standard, surveys were conducted in a form of interviews with 20 subjects who have used the Twitter application to find out problems and solutions for the UX design of the mobile Twitter application.

Reutilization of Organic and Peat Soils by Deep Cement Mixing

Limited infrastructure development on peats and organic soils is a serious geotechnical issues common to many countries of the world especially Malaysia which distributed 1.5 mill ha of those problematic soil. These soils have high water content and organic content which exhibit different mechanical properties and may also change chemically and biologically with time. Constructing structures on peaty ground involves the risk of ground failure and extreme settlement. Nowdays, much efforts need to be done in making peatlands usable for construction due to increased landuse. Deep mixing method employing cement as binders, is generally used as measure again peaty/ organic ground failure problem. Where the technique is widely adopted because it can improved ground considerably in a short period of time. An understanding of geotechnical properties as shear strength, stiffness and compressibility behavior of these soils was requires before continues construction on it. Therefore, 1- 1.5 meter peat soil sample from states of Johor and an organic soil from Melaka, Malaysia were investigated. Cement were added to the soil in the pre-mixing stage with water cement ratio at range 3.5,7,14,140 for peats and 5,10,30 for organic soils, essentially to modify the original soil textures and properties. The mixtures which in slurry form will pour to polyvinyl chloride (pvc) tube and cured at room temperature 250C for 7,14 and 28 days. Laboratory experiments were conducted including unconfined compressive strength and bender element , to monitor the improved strength and stiffness of the 'stabilised mixed soils'. In between, scanning electron miscroscopic (SEM) were observations to investigate changes in microstructures of stabilised soils and to evaluated hardening effect of a peat and organic soils stabilised cement. This preliminary effort indicated that pre-mixing peat and organic soils contributes in gaining soil strength while help the engineers to establish a new method for those problematic ground improvement in further practical and long term applications.

Mutational Effect to Particular Interaction Energy of Cycloguanil Drug to Plasmodium Plasmodium Falciparum Dihydrofolate Reductase Enzymes

In order to find the particular interaction energy between cylcloguanil and the amino acids surrounding the pocket of wild type and quadruple mutant type PfDHFR enzymes, the MP2 method with basis set 6-31G(d,p) level of calculations was performed. The obtained interaction energies found that Asp54 has the strongest interaction energy to both wild type and mutant type of - 12.439 and -11.250 kcal/mol, respectively and three amino acids; Asp54, Ile164 and Ile14 formed the H-bonding with cycloguanil drug. Importantly, the mutation at Ser108Asn was the key important of cycloguanil resistant with showing repulsive interaction energy.

A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm

Fault-proneness of a software module is the probability that the module contains faults. To predict faultproneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, neural network techniques and clustering techniques. The aim of proposed study is to explore whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using Genetic Algorithm technique. This approach has been tested with real time defect C Programming language datasets of NASA software projects. The results show that the fusion of requirement and code metric is the best prediction model for detecting the faults as compared with commonly used code based model.

A Sufficient Condition for Graphs to Have Hamiltonian [a, b]-Factors

Let a and b be nonnegative integers with 2 ≤ a < b, and let G be a Hamiltonian graph of order n with n ≥ (a+b−4)(a+b−2) b−2 . An [a, b]-factor F of G is called a Hamiltonian [a, b]-factor if F contains a Hamiltonian cycle. In this paper, it is proved that G has a Hamiltonian [a, b]-factor if |NG(X)| > (a−1)n+|X|−1 a+b−3 for every nonempty independent subset X of V (G) and δ(G) > (a−1)n+a+b−4 a+b−3 .

Agents Network on a Grid: An Approach with the Set of Circulant Operators

In this work we present some matrix operators named circulant operators and their action on square matrices. This study on square matrices provides new insights into the structure of the space of square matrices. Moreover it can be useful in various fields as in agents networking on Grid or large-scale distributed self-organizing grid systems.

Relative Radiometric Correction of Cloudy Multitemporal Satellite Imagery

Repeated observation of a given area over time yields potential for many forms of change detection analysis. These repeated observations are confounded in terms of radiometric consistency due to changes in sensor calibration over time, differences in illumination, observation angles and variation in atmospheric effects. This paper demonstrates applicability of an empirical relative radiometric normalization method to a set of multitemporal cloudy images acquired by Resourcesat1 LISS III sensor. Objective of this study is to detect and remove cloud cover and normalize an image radiometrically. Cloud detection is achieved by using Average Brightness Threshold (ABT) algorithm. The detected cloud is removed and replaced with data from another images of the same area. After cloud removal, the proposed normalization method is applied to reduce the radiometric influence caused by non surface factors. This process identifies landscape elements whose reflectance values are nearly constant over time, i.e. the subset of non-changing pixels are identified using frequency based correlation technique. The quality of radiometric normalization is statistically assessed by R2 value and mean square error (MSE) between each pair of analogous band.

Effective Scheduling of Semiconductor Manufacturing using Simulation

The process of wafer fabrication is arguably the most technologically complex and capital intensive stage in semiconductor manufacturing. This large-scale discrete-event process is highly reentrant, and involves hundreds of machines, restrictions, and processing steps. Therefore, production control of wafer fabrication facilities (fab), specifically scheduling, is one of the most challenging problems that this industry faces. Dispatching rules have been extensively applied to the scheduling problems in semiconductor manufacturing. Moreover, lot release policies are commonly used in this manufacturing setting to further improve the performance of such systems and reduce its inherent variability. In this work, simulation is used in the scheduling of re-entrant flow shop manufacturing systems with an application in semiconductor wafer fabrication; where, a simulation model has been developed for the Intel Five-Machine Six Step Mini-Fab using the ExtendTM simulation environment. The Mini-Fab has been selected as it captures the challenges involved in scheduling the highly re-entrant semiconductor manufacturing lines. A number of scenarios have been developed and have been used to evaluate the effect of different dispatching rules and lot release policies on the selected performance measures. Results of simulation showed that the performance of the Mini-Fab can be drastically improved using a combination of dispatching rules and lot release policy.

A Novel Prediction Method for Tag SNP Selection using Genetic Algorithm based on KNN

Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Improvement of Photoluminescence Uniformity of Porous Silicon by using Stirring Anodization Process

The electrolyte stirring method of anodization etching process for manufacturing porous silicon (PS) is reported in this work. Two experimental setups of nature air stirring (PS-ASM) and electrolyte stirring (PS-ESM) are employed to clarify the influence of stirring mechanisms on electrochemical etching process. Compared to traditional fabrication without any stirring apparatus (PS-TM), a large plateau region of PS surface structure is obtained from samples with both stirring methods by the 3D-profiler measurement. Moreover, the light emission response is also improved by both proposed electrolyte stirring methods due to the cycling force in electrolyte could effectively enhance etch-carrier distribution while the electrochemical etching process is made. According to the analysis of statistical calculation of photoluminescence (PL) intensity, lower standard deviations are obtained from PS-samples with studied stirring methods, i.e. the uniformity of PL-intensity is effectively improved. The calculated deviations of PL-intensity are 93.2, 74.5 and 64, respectively, for PS-TM, PS-ASM and PS-ESM.

Fuzzy Logic Speed Control of Three Phase Induction Motor Drive

This paper presents an intelligent speed control system based on fuzzy logic for a voltage source PWM inverter-fed indirect vector controlled induction motor drive. Traditional indirect vector control system of induction motor introduces conventional PI regulator in outer speed loop; it is proved that the low precision of the speed regulator debases the performance of the whole system. To overcome this problem, replacement of PI controller by an intelligent controller based on fuzzy set theory is proposed. The performance of the intelligent controller has been investigated through digital simulation using MATLAB-SIMULINK package for different operating conditions such as sudden change in reference speed and load torque. The simulation results demonstrate that the performance of the proposed controller is better than that of the conventional PI controller.

Simulation and Design of the Geometric Characteristics of the Oscillatory Thermal Cycler

Since polymerase chain reaction (PCR) has been invented, it has emerged as a powerful tool in genetic analysis. The PCR products are closely linked with thermal cycles. Therefore, to reduce the reaction time and make temperature distribution uniform in the reaction chamber, a novel oscillatory thermal cycler is designed. The sample is placed in a fixed chamber, and three constant isothermal zones are established and lined in the system. The sample is oscillated and contacted with three different isothermal zones to complete thermal cycles. This study presents the design of the geometric characteristics of the chamber. The commercial software CFD-ACE+TM is utilized to investigate the influences of various materials, heating times, chamber volumes, and moving speed of the chamber on the temperature distributions inside the chamber. The chamber moves at a specific velocity and the boundary conditions with time variations are related to the moving speed. Whereas the chamber moves, the boundary is specified at the conditions of the convection or the uniform temperature. The user subroutines compiled by the FORTRAN language are used to make the numerical results realistically. Results show that the reaction chamber with a rectangular prism is heated on six faces; the effects of various moving speeds of the chamber on the temperature distributions are examined. Regarding to the temperature profiles and the standard deviation of the temperature at the Y-cut cross section, the non-uniform temperature inside chamber is found as the moving speed is larger than 0.01 m/s. By reducing the heating faces to four, the standard deviation of the temperature of the reaction chamber is under 1.4×10-3K with the range of velocities between 0.0001 m/s and 1 m/s. The nature convective boundary conditions are set at all boundaries while the chamber moves between two heaters, the effects of various moving velocities of the chamber on the temperature distributions are negligible at the assigned time duration.

Communicative Competence: Novice versus Professional Engineers' Perceptions

The notion of communicative competence has been deemed fuzzy in communication studies. This fuzziness has led to tensions among engineers across tenures in interpreting what constitutes communicative competence. The study seeks to investigate novice and professional engineers- understanding of the said notion in terms of two main elements of communicative competence: linguistic and rhetorical competence. Novice engineers are final year engineering students, whilst professional engineers represent engineers who have at least 5 years working experience. Novice and professional engineers were interviewed to gauge their perceptions on linguistic and rhetorical features deemed necessary to enhance communicative competence for the profession. Both groups indicated awareness and differences on the importance of the sub-sets of communicative competence, namely, rhetorical explanatory competence, linguistic oral immediacy competence, technical competence and meta-cognitive competence. Such differences, a possible attribute of the learning theory, inadvertently indicate sublime differences in the way novice and professional engineers perceive communicative competence.

Reducing SAGE Data Using Genetic Algorithms

Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved.