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.

In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513.

The paper presents the implementation of Object-Oriented (OO) integrated approaches to the design of scalable Electro-Cardio-Graph (ECG) Systems. The purpose of this methodology is to preserve real-world structure and relations with the aim to minimize the information loss during the process of modeling, especially for Real-Time (RT) systems. We report on a case study of the design that uses the integration of OO and RT methods and the Unified Modeling Language (UML) standard notation. OO methods identify objects in the real-world domain and use them as fundamental building blocks for the software system. The gained experience based on the strongly defined semantics of the object model is discussed and related problems are analyzed.

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.

The problem of predicting protein structure based on amino acids sequence is one of the most interesting problems in molecular biology. Determination of the protein structure based on the experimental methods is very expensive, requires significant time and needs experts from different fields. For some types of proteins, theoretically based method for structure prediction is one of the alternatives. In this paper we describe the method and results of using CB513 as a dataset suitable for development of artificial neural network algorithms for prediction of secondary protein structure with MATLAB-Neural Network Toolbox.

Track or road layout in a given geographical area to plan new or improve existing public and/or private transportation systems is a complex problem. Especially today, many parameters have to be considered, and some of them are not obvious. For example, in face of an imminent energy crisis, the energy consumption of a transportation system should be minimized. In general, the resulting layout has to be an adequate compromise of many parameters. This means that there are many possible ways to solve the problem, which quality differs. Consequently, this type of problem implies an explosion of search-space states with raising number of midpoints and/or resolution. In these cases, heuristic search methods have their major advances compared to non-heuristic search methods, which would need time and memory proportional to the search-space size to find the best solution. We explored, in particular, the use of a genetic algorithm, as a representative of the class of evolutionary algorithms. It searches the search-space selectively by focusing on interesting regions, constantly trying to find even better regions while having significantly less memory requirements. The importance of its main parameters, their impact on the performance and the precision of the genetic algorithm are presented in this paper. Guidelines for a good set-up of the genetic algorithm are given in order to gain high efficiency and effectiveness; the encoding of parameters, the build-up of the fitness function, evaluation and reproduction of chromosomes, elitism and convergence affinity as the main engine of genetic algorithms is discussed in detail as are the limits of this approach. The modified and improved simple genetic algorithm to solve the mentioned track-layout problem forms another core part of the paper.

For medical volume visualization, one of the most important tasks is to reveal clinically relevant details from the 3D scan (CT, MRI ...), e.g. the coronary arteries, without obscuring them with less significant parts. These volume datasets contain different materials which are difficult to extract and visualize with 1D transfer functions based solely on the attenuation coefficient. Multi-dimensional transfer functions allow a much more precise classification of data which makes it easier to separate different surfaces from each other. Unfortunately, setting up multi-dimensional transfer functions can become a fairly complex task, generally accomplished by trial and error. This paper explains neural networks, and then presents an efficient way to speed up visualization process by semi-automatic transfer function generation. We describe how to use neural networks to detect distinctive features shown in the 2D histogram of the volume data and how to use this information for data classification.

This paper describes basic components and principles for support of the normatively regulated organizational activities. These activities are characterized by precise objective or purpose, participation of actors as role-holders, and norms and rules that govern the performance of these activities. Particular aspect and modeling of the normatively regulated activities are presented. Some aspects of object view on normatively regulated activities are described, with particular case of procurement activity.

This paper presents application of a fuzzy logic in urban planning. Urban environment quality evaluation is an important part of environmental planning and management. Traditional theory does not give as good evaluation as the fuzzy set theory, which provides the basis for urban planning. Most information related to environmental evaluation has the spatial component, which is why GIS is widely used in the evaluation of the environment. Integration of the GIS and fuzzy set theory has been used lately in evaluation of urban environment quality. Fuzzy set theory is used in analysis of the environment because of its ability to manage imprecise, insecure and ambiguous data.

This paper presents application of a fuzzy logic based system to automatically evaluate the maintainability of code. Code evaluation is accomplished by rating its quality provided with bad smells in code as inputs. Straightforward bad smells with existing software metrics tools are selected as inputs: duplicated code, long methods, large classes having a high cyclomatic complexity, or a large number of parameters and temporary fields. Removing these bad smells can result in significant code improvements concerning readability and maintainability. However, the precise definition of attributes like small, long, large or high is not clear, and their identification is rather subjective. Fuzzy logic values are suitable for capturing partial correspondence to attributes and fuzzy rules model have been used to describe the relation between bad smells and code quality. Model supporting the experimental evaluation of the fuzzy based code evaluation is implemented in Java.

This paper deals with both introducing novel technique of calculating population diversity and analyzing the existing ones. This motivation to investigate new methods of determining population diversity lies in significant disadvantages of commonly used techniques, particularly the ones that operate in a parameter space. The problem with these methods is that they can produce inexact information about population state, e.g. indicate high diversity when it is far from being high. For the purpose of eliminating these problems, new diversity mechanisms are investigated. The main idea was to use the information that is contained in the matrix with all mutual distances between individuals. New mechanism can be employed within a standard parallel search algorithms (whether as analyzing or guiding mechanism), or in general, as a mechanism for determining how well does the finite set of points sample a compact region of space.

(MSc EE Almir Karabegovic, Gauss, Geo Information Systems, Stupine B9/6, Tuzla – Bosnia and Herzegovina, almir@gauss.ba) (PhD Zikrija Avdagic, Faculty of Electrical Engineering, Department for Informatics and Computer Science, Zmaja od Bosne (Kampus), Sarajevo – Bosnia and Herzegovina, zikrija.avdagic@etf.unsa.ba) (MSc EE Mirza Ponjavic, Gauss, Geo Information Systems, Stupine B9/6, Tuzla – Bosnia and Herzegovina, mirza@gauss.ba)

This paper is focused on the development of methodology for multicriterial land valorization in land use planning by application of genetic algorithm. One of the key tools for design of the decision support system based on this methodology is geographic information system which serve to quantify multicriterial data and represent resulting spatial data. The methodology and the algorithm are applied to a specific problem of spatial planning in Tuzla Canton, Bosnia and Herzegovina. The crucial points of the research are the following: possibility of multicriterial valorization of the land from the GA use perspective, how to utilize the capacity of the GA optimization techniques in the frame of decision support system and with usage of the GIS tools and how to apply the GA in the field of genotype presentation in spatial modeling.