The need for data storage is related to the very beginning of digital data processing. Data were first stored on floppy disks, magnetic tape and at the end on hard diskc. Today, in order to save data we can use some of traditional storage systems like DAS, NAS, SAN, Client server systems and peer to peer systems. Peer to peer systems have resources which they share with other peers on network. Existing peer to peer systems are based on WAN connection links, implementing overlay network in order to achieve connectivity with logical addressing scheme. The most important issue that peer to peer systems have to provide is availability of data using some sort of replication and redudancy. Usually this is implemented using file replication or erasure code. In this paper we will present design of peer to peer based distributed system for data storage inside local computer networks, with file replication. File replication relies on introduced algorithm for storage management.

Evolutionary strategy is here used in searching objective function for two phase integer linear programming approach to solving the school time tabling problem. This allows improvements in generated timetables even for criteria, which are not easily expressed by ordinary objective function.

This paper discusses some of the interesting properties of stability analysis of a discretized wave equation. The solutions of the wave equation are wave functions, hence oscillating, so when testing stability the discretization scheme usually shows marginal stability. Marginal stability is a sufficient condition for a discrete scheme convergence and many authors don't bother with mathematical consistency. However, inadequatly chosen discretization method may lead to the additional unwanted oscillations. This paper illustrates this effect in a different approach. First, the wave equation is introduced together with a Perfectly matched layer (PML). Then the 1D wave equation is discretized by using Finite Differences Method (FDM) and Finite-differences Time-domain method (FDTD). It is shown that the latter method does not produce spurious oscillations in the solution. Eigenvalue analysis is done to explain this effect and discuss stability of the numerical scheme.

This paper engages the problem of adjusting the zero-order Sugeno fuzzy reasoning, so it could work on relatively simple and cheap microelectronic circuit, such as microcontoller. In this paper, a way to present the fuzzy system in a convinient form for storage in the microcontroller memory is shown. Also it was shown that the whole fuzzy reasoning can be reduced to operating exclusively on integer numbers.

Machine learning is very important in several fields ranging from control systems to data mining. This paper presents Q — Learning implementation for abstract graph models with maze solving (finding the trajectory out of the maze) taken as example of graph problem. The paper consists of conversion of maze matrix to Q — Learning reward matrix, and also the implementation of Q — Learning algorithm for the reward matrix (similar to minimizing criteria matrix in dynamic programming). This implementation is on higher level of abstraction, so other representations can be used (artificial neural networks, tree etc.). For the testing of Q — Learning algorithm, maze solving problem was visualized in MATLAB programming language with the found trajectory marked on the maze. The maze in this paper is defined with starting position in the top left corner and the exit in the bottom right corner. The performance of the algorithm is measured for different scales of the problem.

This paper presents a sound field analysis method which uses numerical solving of the wave equation. The wave equation is described, its analytical and numerical solutions, and its application in sound field analysis. Finite difference method is used for solving the equation. Comparison of analytical and numerical solutions is shown, together with a discussion on convergence and stability of the numerical solution. Finally, the sound field simulator based on numerical solving of wave equation is presented. Applicability of sound field simulator for developing object detection algorithms is also analyzed.

This paper demonstrates the effectiveness of a genetic algorithm in identification of the unknown parameters of a nonlinear 2DOF laboratory helicopter model. The mathematical model of physical structure of the helicopter has fourteen unknown parameters which are necessary to be identified. For identification of this model a genetic algorithm is chosen because it enables finding referent results using less number of the experiments in comparison with other identification techniques. After the identification process has been carried out, the unknown parameters are determined and validated through comparisons of the simulation model response and response of the real helicopter system.

In this paper, we propose one model for high school timetable generation, which uses two-phase linear integer programming to solve the problem. This reduces the required computation time, by decomposing the problem to determine the day and then, in the second phase, to generate a daily schedule. The approach was demonstrated on a test problem, and the results for various settings of the model parameters are presented.

The paper describes a digital PID controller realized in the framework of a student project at the Department of Automatic Control and Electronics of Faculty of Electrical Engineering in Sarajevo. This project included the complete solution, from hardware design to software implementation of control algorithms.

This document proposes an approach for financial statements' anomalies detection by using on-line evolving clustering [1]. Official records of the financial activities of a business are called financial statements and they are recorded in journals and general ledger in a supervised process. Anomalies in financial statements are caused by human mistakes during forming of financial statements, or as a result of changes in the software that produced un-expected errors, or as possible financial fraud.

This paper describes the application of a parallel genetic algorithm that solves the weekly timetable construction problem for elementary schools. Timetable construction is NP-complete and highly constrained problem, and therefore represents a computationally intensive task. A Parallel Genetic Algorithm (PGA) is proposed with specific methods for chromosome representation and fitness evaluation, and specific recombination and mutation operators. The proposed solution uses a coarse grained PGA, which is suitable for execution on a Beowulf cluster. Experimental results are provided, with a comparison of serial and parallel execution times for the same algorithm.

This paper describes how a non-stationary multi-objective optimization model can be used for synthesis of control of mobile robot in unknown environment. The modelled problem is solved using multi-objective genetic algorithm (MOGA). Results of experiments conducted in simulation environment demonstrate the application of the described approach.

This paper presents two approaches to isolation of vibrations of driver's seat using controller based on artificial neural networks (ANNs). The quality of isolation is measured using a standardized technique. The results of simulation in Matlab/Simulink, as well as the results of implemented controllers on a real experimental model are presented.

The purpose of this study is to model and optimize the detection of tar in cigarettes during the manufacturing process and show that low yield cigarettes contain similar levels of nicotine as compared to high yield cigarettes while B (Benzene), T(toluene) and X (xylene) (BTX) levels increase with increasing tar yields. A neuro-fuzzy system which comprises a fuzzy inference structure is used to model such a system. Given a training set of samples, the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifiers learned how to differentiate a new case in the domain. The ANFIS classifiers were used to detect the tar in smoke condensate when five basic features defining cigarette classes indications were used as inputs. A classical method by High Performance Liquid Chromatography (HPLC) is also introduced to solve this problem. At last the performances of these two methods are compared.