Computational Science and Techniques

This work put forwards an optimal BCI (Brain Computer Interface) speller design based on Steady State Visual Evoked Potentials (SSVEP) and Artificial Neural Network (ANN) in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM) of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.

Cloud computing can be defined as a new style of computing in which dynamically scala-ble and often virtualized resources are provided as a services over the Internet. Advantages of the cloud computing technology include cost savings, high availability, and easy scalability. Voas and Zhang adapted six phases of computing paradigms, from dummy termi-nals/mainframes, to PCs, networking computing, to grid and cloud computing. There are four types of cloud computing: public cloud, private cloud, hybrid cloud and community. The most common and well-known deployment model is Public Cloud. A Private Cloud is suited for sensitive data, where the customer is dependent on a certain degree of security.

The paper proposes a technology for mass optimization of two-dimensional body applying genetic algorithms. Main attention is focused on geometry of 2D body, i. e. search for optimal coordinates of body points. Direct analysis of 2D body – von Mises stress determination – is performed using original program based on finite element method. The set of design parameters contains the coordinates of body points in 2D space. The results of numerical experiments proved the proposed technology to be efficient tool for solution of 2D body mass optimization problem.

In this paper a stochastic adaptive method has been developed to solve stochastic linear problems by a finite sequence of Monte-Carlo sampling estimators. The method is based on the adaptive regulation of the size of Monte-Carlo samples and a statistical termination procedure taking into consideration statistical modelling accuracy. Our approach distinguishes itself by the treatment of accuracy of the solution in a statistical manner, testing the hypothesis of optimality according to statistical criteria, and estimating confidence intervals of the objective and constraint functions. To avoid “jamming” or “zigzagging” solving a constraint problem we implement the ε–feasible direction approach. The proposed adjustment of a sample size, when it is taken inversely proportional to the square of the norm of the Monte-Carlo estimate of the gradient, guarantees convergence a. s. at a linear rate. The numerical study and examples in practice corroborate theoretical conclusions and show that the developed procedures make it possible to solve stochastic problems with sufficient accuracy by the means of an acceptable size of computations.

Mathematical model of biosensor with competitive substrates conversion is analysed in this work. Model is described by partial differential reaction-diffusion equations with non-linear reaction term. Because of the non-linearity the analytical solutions exist only for extreme parameter values and thus the model in general case is solved by finite difference methods. The validity of the computational model is checked by comparing numerically obtained results to the known analytical solutions at the mentioned extreme parameter values. The purpose of this work is to determine the values of model parameters at which the impact of one of the substrates on the biosensor response can be minimized.

The current paper illustrates the importance of clustering the frequent items of code coverage during test suite reduction. A modular Most maximal frequent sequence clustered algorithm has been used along with a Requirement residue based test case reduction process. DU-pairs form the basic code coverage requirement under consideration for test suite reduction. This algorithm farewell when compared with few other algorithms like Harrold Gupta and Soffa (HGS) and Bi-Objective Greedy (BOG) algorithms and Greedy algorithms in covering all the DU-Pairs. The coverage criteria achieved is 100% in many cases, except for few insufficient and incomplete test suites.

The article deals with interaction of tumour cells and leucocytes in the cylindrical cavities. This type of interaction is typical in the cases of development of a tumour in the intestine, blood vessel or in a bone cavity. Two cases are separated: the case of soft and hard tumour. In the case of a solid tumour, leucocytes can interact only with the surface cells of the tumour. This type of interaction is described by the system of two nonlinear first degree differential equations. The expressions of stationary points are obtained and analysis of their stability is performed. In the case of a soft tumour the system of two partial differential equations with first order derivatives and initial and boundary conditions is proposed. An algorithm for computing the numeric solution of the mathematical model is applied. In this case the diffusion of leucocytes and their ability to reach the tumour cells in the whole volume of the tumour is included. The algorithm is constructed and the system is solved numerically. Bifurcation curve is obtained. It separates two qualitatively different areas on the two parameter plane. Under the same initial parameters in the first area development of the tumour cells cannot be stopped, whereas in the second area leukocytes defeat the tumour cells.

Service oriented architecture (SOA) is an architecture for distributed applications composed of distributed services with weak coupling that are designed to meet business requirements. One of the research priorities in the field of SOA is creating such software design and development methodology (SDDM) that takes into account all principles of this architecture and allows for effective and efficient application development. A lot of investigation has been carried out to find out whether can one of popular SDDM, such as agile methodologies or RUP suits, be adapted for SOA or there is a need to create some new SOA-oriented SDDM. This paper compares one of SOA-oriented SDDM – SOUP – with RUP and XP methodologies. The aim is to find out whether the SOUP methodology is already mature enough to assure successful development of SOA applications. This aim is accomplished by comparing activities, artifacts of SOUP and RUP and emphasizing which XP practices are used in SOUP.

The amount of information on the internet grows exponentially. It isnot enough anymore just to have a general access to this huge amount of data,instead it is becoming a necessity to be able to use different kinds ofautomatic filters to retrieve just the information you actually want. One solution for the information filtering and retrieval is context analysis in which one of the contexts of interest is the geographic context. This paper studies the problem and methodology of geoparsing – recognition of geographic names in unstructured textual content for the aim of extracting geographic context. A prototype implementation of a geoparsing system, capable of automatically analyzing unstructured text, recognizing geographic information and marking geographic names, is developed. Empirical evaluation of the system using articles from real-world news showed that the average quality of its geographic name recognition varies around 75-100%. Possible applications of the developed prototype include automated grouping of any texts by their geographic contexts (e.g., in news portals) and location-based search. Preliminary results of empirical evaluation showed that the average rate of its geographic name recognition varies around 75-100%.

This work contains Monte–Carlo Markov Chain algorithm for estimation of multi-dimensional rare events frequencies. Logits of rare event likelihood we are modeling with Poisson distribution, which parameters are distributed by multivariate normal law with unknown parameters – mean vector and covariance matrix. The estimations of unknown parameters are calculated by the maximum likelihood method. There are equations derived, those must be satisfied with model’s maximum likelihood parameters estimations. Positive definition of evaluated covariance matrixes are controlled by calculating ratio between matrix maximum and minimum eigenvalues.