Abstract: In the multi-attribute utility theory the utility functions are usually constructed by dots. It concerns both the lottery’s method and the value increasing method. In the both cases the utility function is constructed in the absolute scale 0,1 that causes inconveniences for experts. The comparative assessments look more preferable for decision-makers. The paired comparison matrix (PCM) looks as a natural model representing the preference structure of decision-maker (DM). We use scale points of attributes as a PCM comparative entities. We use also increasing/decreasing entity priority as a criterion. Function of priorities is transformed to utility function on the base of a normalizing function. Such a function allows using the matrix power as parameter affecting the form of utility function. The PCM provides the extended possibilities to DMs to form comparative assessments both the qualitative ones (as better-worse) and the quantitative ones reflecting winnings and losses of DMs. In the paper we consider methods for utility function construction having different forms of its presentation. Among them there are utility functions based on attributes measured in nominal scales.

Keywords: utility function, paired comparison matrix, scale points, priority function.

ACM Classification Keywords: H.4.2 Information Systems Applications: Types of Systems-decision support

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UTILITY FUNCTION DESIGN ON THE BASE OF THE PAIRED COMPARISON MATRIX*

Stanislav Mikoni

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p23.pdf

Abstract: This paper presents an exploratory study of the effectiveness of support vector machines used as a tool for computer-aided breast cancer diagnosis. We explore the discriminatory power of heterogeneous mammographic and sonographic descriptors in solving the classification task. Various feature selection techniques were tested to find a set of descriptors that outperforms those from similar studies. We also explored how choice of the SVM kernel function and model parameters affect its predictive abilities. The kernels explored were linear, radial basis function, polynomial, and sigmoid. The model performance was estimated by ROC analysis and metrics, such as true and false positive rates, maximum accuracy, area under the ROC curve, partial area under the ROC curve with sensitivity above 90%, and specificity at 98% sensitivity. Particular attention was paid to the latter two as lack of specificity causes unnecessary surgical biopsies. Experiments registered that an appropriate reduction of variables can greatly improve the predictive power of the model, as long as the choice of the kernel affects the model performance marginally. We also found that the SVM is superior to the common classification technique used in the field - MLP neural networks.

Keywords: data mining, support vector machines, heterogeneous data; breast cancer diagnosis, computer aided diagnosis.

ACM Classification Keywords: I.5.2- Computing Methodologies - Pattern Recognition – Design Methodology - Classifier design and evaluation.

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SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF MALIGNANT AND BENIGN LESIONS

Anatoli Nachev, Mairead Hogan

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p22.pdf

Abstract: The use of Machine Learning (ML) techniques is already widespread in Medicine Diagnosis. The use of these techniques helps increasing the efficiency of human diagnostic, which is significantly affected by the human conditions such as stress as well as the lack of experience. In this paper, integration between two ML techniques case-based reasoning (CBR) and artificial neural network (ANN) is used for the automation of the diagnosis of pathology on the vertebral column. CBR is used for indexing and retrieval. For adaptation, an untrained ANN is fed with the retrieved closest matches. Then the ANN is trained and queried with the new problem to give the adapted solution. Experiments are conducted on the vertebral column data set from University of California Irvine (UCI) machine learning repository. A comparison with several machine learning techniques used for classifying the same problem is performed. Results show that the hybridization between CBR and ANN helps in improving the classification.

Keywords: Computer Aided Diagnosis System, Hybrid Intelligent Classifier, Vertebral Column, Case-Based? Reasoning, Artificial Neural Network.

ACM Classification Keywords: I.2.5 Expert system tools and techniques - Conference proceedings.

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A HYBRID INTELLIGENT CLASSIFIER FOR THE DIAGNOSIS OF PATHOLOGY ON THE VERTEBRAL COLUM

Essam Abdrabou

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p21.pdf

Abstract: The clustering problem for multivariate observations often encountered in many applications connected with Data Mining and Exploratory Data Analysis. Conventional approach to solving these problems requires that each observation may belong to only one cluster, although a more natural situation is when the vector of features with different levels of probabilities or possibilities can belong to several classes. This situation is subject of consideration of fuzzy cluster analysis, intensively developing today. In many practical tasks of Data Mining, including clustering, data sets may contain gaps, information in which, for whatever reasons, is missing. More effective in this situation are approaches based on the mathematical apparatus of Computational Intelligence and first of all artificial neural networks and different modifications of classical fuzzy c-means (FCM) method. But these methods are effective only in cases when the original data set is given beforehand and does not change during data processing. At the same time there is a wide class of problems when the data are fed to processing sequentially in on-line mode as it occurs in self-organizing Kohonen networks training. At the same time apriori it is not known which of the vectors-images contain gaps. In this paper the problem of probabilistic and possibilistic on-line clustering of data with gaps using Partial Distance Strategy is discussed and solved, self-organizing neuro-fuzzy Kohonen network and new self-learning algorithm that is the hybrid of "Winner-takes-more" rule and recurrent fuzzy clustering procedures are proposed and investigated.

Keywords: Fuzzy clustering, Kohonen self-organizing network, learning rule, incomplete data with gaps.

ACM Classification Keywords: 1.2.6 Artificial Intelligence: Learning – Connectionism and neural nets; 1.2.8 Artificial Intelligence: Problem Solving, Control Methods, and Search – Control theory; 1.5.1 Pattern Recognition: Clustering – Algorithms.

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ADAPTIVE CLUSTERING OF INCOMPLETE DATA USING NEURO-FUZZY KOHONEN NETWORK

Yevgeniy Bodyanskiy, Alina Shafronenko, Valentyna Volkova

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p20.pdf

Abstract: The paper presents the experiment on the time series whose elements are month values of BIS effective exchange rate of USD from January 1994 till March 2010. A tendency of BIS (Bank of International Settlements) effective exchange rate to increase or decrease is an expected value. First, a process of building of the neural network for events forecasting is presented, that is the selection of networks’ architecture and parameters. Next, the effect of adding data calibrated by nonlinear input function to input data calibrated linearly is described. The nonlinear input function - hyperbolic tangent was accepted. Hyperbolic tangent sigmoid transfer function and log sigmoid transfer function are commonly used as transfer functions in neural networks.

Keywords: neural network, time series.

ACM Classification Keywords: I.2.8 Data calibration.

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THE EFFECT OF INTRODUCTION OF THE NON-LINEAR CALIBRATION FUNCTION AT THE INPUT OF THE NEURAL NETWORK

Piotr Romanowski

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p19.pdf

Abstract: The main goal of this paper is to present and evaluate the possibility of using the methods and tools of Artificial Intelligence and Data Mining to analyze marketing data needed to support decision-making in the process of market segmentation. This paper describes the application of Kohonen’s Neural Networks and Classification Trees (including tools such as CART-Classification and Regression Tree, Chi-squared Automatic Interaction Detector (CHAID) and Boosted Tree) to solving problems of classification and grouping of data. The main part presents the results of market segmentation that can be used by the company producing household products. Finally conclusions and further research plans have been described.

Keywords: data analysis, artificial intelligence, data mining, classification, clustering, Kohonen’s neural networks.

ACM Classification Keywords: I.2.m Miscellaneous : I.2.6 Artificial Intelligence: Learning – Connectionism and neural nets; I.5.1: Models – Neural nets; I.5.3: Clustering – Algorithms.

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INTELLIGENT ANALYSIS OF MARKETING DATA

Łukasz Paśko, Galina Setlak

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p18.pdf

Abstract: The Effective methods of intelligent analysis of business processes, in particular, methods of revealing fragments of such processes are developed. Besides, analyzing information extracted from journals of registering events of a business process (BP) to formalize the real behavior of a BP is carried out. Such data analysis is especially important in those cases when the occurring sequence of events is registered, i.e. executives have an opportunity to make a decision about the order of further process implementation.

Keywords: business process, procedure, logical net, intelligent analysis.

ACM Classification Keywords: I.2 Artificial Intelligence – Knowledge Representation Formalisms and Methods

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INTELLIGENT METHODS OF REVEALING FRAGMENTS IN BUSINESS PROCESSES

Nataliia Golian, Vira Golian, Olga Kalynychenko

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p17.pdf

Abstract. The main goal of BI systems is to provide the access for the users to crucial information connected with the tools they use every day. It allows to take more relevant decisions, share knowledge with other people, cooperate within the whole organization and increase the company's gainings. The offered functionality includes either the scalable technology's platforms designed for workers in all management tiers.

Keywords: Business Intel's lIntelligence, Business Discovery, information, analysis, Qlickview

ACM Classification Keywords: K.6 Management of computing and information systems - K.6.0 General economics

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BI – SUPPORTING THE PROCESSES OF THE ORGANIZATION'S KNOWLEDGE MANAGEMENT

Justyna Stasieńko

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p16.pdf

Abstract: Inductive modelling tools are widely used for solving problems of analysing economical, ecological, and other processes. Development of business intelligence systems based on inductive modelling algorithms for analysis, modelling, forecasting, classification, and clustering of complex processes is very promising. When solving real tasks of model construction from statistical data, the question of storage of and providing effective access to the information arises. At the stage of input data processing there are typical difficulties with processing data in different formats as well as containing omissions and untypically small or big values etc. From the other side, the question of output information storage exists like determination of structure and parameters of models, estimation of precision and validity, plots and diagrams drawing etc. This would allow structuring input data of different types and using the information already existing in database and also provide the storage of complete information on experiments and results of calculations. To solve such kind of problems, the integrated environment for storing and handling information is developed. Architecture of the environment is offered giving the possibilities to manipulate present information freely using relational database containing only metadata and storing input statistical data and output results of calculations.

Keywords: integrated environment, handling and storing information, inductive modeling, GMDH-algorithms, Business Intelligence

ACM Classification Keywords: H.2.8 Data Base Application – Data Mining

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INTEGRATED ENVIRONMENT FOR STORING AND HANDLING INFORMATION IN TASKS OF INDUCTIVE MODELLING FOR BUSINESS INTELLIGENCE SYSTEMS

Nataliya Shcherbakova, Volodymyr Stepashko

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p15.pdf

Abstract: The classical Forrester model of world dynamics is a system of 5 differential equations related with 5 macro-economical variables (population, resources, etc.). This model was developed at 1970-1971 but by the moment its stability to noise was not studied. The plan of experiments is described and the results of modeling are presented. It proved that a) noise affects stronger initial data then the model during its functionality b) change of resources is the most critical value in comparison with the other system variables. All experiments have been made by means of the program WorldDyn? developed on MatLab?.

Keywords: Forrester model, word dynamics noise immunity, numerical analysis

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TESTING STABILITY OF THE CLASSICAL FORRESTER MODEL TO INITIAL DATA AND ADDITIVE NOISE

Olga Proncheva, Mikhail Alexandrov, Sergey Makhov

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p14.pdf

Abstract: At far 1970 the elite Roman Club asked prof. J. Forrester from MIT to develop a model of world dynamics. Speaking world dynamics we mean the dynamic interactivity of the main macro economical variables. The 1-st version of the model named “World-1” was presented in 4 weeks and next year the corrected version “World-2” was accepted as the classical J. Forrester’s model. In spite of its long history the J. Forrester model retains its actuality being the basis for modern models. In the paper we consider the principal of system dynamic, criticism of the classical model, and the new models developed by the J. Forrester’s followers. We consider also adjacent areas and open problems related with world dynamics.

Keywords: world dynamics, non lineal dynamics, J. Forrester model

ACM Classification Keywords: I.2.m Miscellaneous

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J. FORRESTER’S MODEL OF WORLD DYNAMICS AND ITS DEVELOPMENT (REVIEW)

Olga Proncheva, Sergey Makhov

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p13.pdf

Abstract: We investigate a possible way for solving the problem of combination of logical inference search methods and symbolic computation tools in e-learning testing on the basis of the approaches developed at the Kiev schools of automated theorem proving and analytical transformations. The investigations started in the first half of 1960s at the Institute of Cybernetics of the Academy of Sciences of Ukraine. Some years later the Faculty of Cybernetics of the Kiev State University was involved in the corresponding projects. The current state of investigations on the topic as well as their theoretical and practical background is described in the paper.

Keywords: analytical transformation, automated theorem proving, deduction, e-learning, intelligent tutoring system.

ACM Classification Keywords: I.2.3 Deduction and Theorem Proving – Deduction. I.2.4 Knowledge Representation Formalisms and Methods – Predicate logic. G.4 Mathematical software. K.3.2 Computer and Information Science Education.

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ON COMBINATION OF DEDUCTION AND ANALYTICAL TRANSFORMATIONS IN E-LEARNING TESTING

Vitaly Klimenko, Alexander Lyaletski, Mykola Nikitchenko

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p12.pdf

Abstract: In the paper global modeling of complex systems with regard to quality of local models of simple plants is discussed. Complex systems consists of several sub-systems. As a global model multilayer feedforward neural networks are used. It is desirable to obtain an optimal global model, as well as optimal local models. A synthetic quality criterion as a sum of the global quality criterion and local quality criteria is defined. By optimization of the synthetic quality criterion can be obtained the global model with regard to the quality of local models of simple plants. The quality criterion of the global model contains coefficients which define the participation of the local quality criteria in the synthetic quality criterion. The investigation of influence of these coefficients on the quality of the global model of the complex static system is discussed. The investigation is examined by a complex system which is composed from two nonlinear simple plants. In this paper complex system means real chemical object (i.e. a part of the line production of ammonium nitrite).

Keywords: complex system, neural network, global modeling

ACM Classification Keywords: I.2.6 ARTIFICIAL INTELLIGENCE, Learning - Connectionism and neural nets

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STUDY THE QUALITY OF GLOBAL NEURAL MODEL WITH REGARD TO LOCAL MODELS OF CHEMICAL COMPLEX SYSTEM

Grzegorz Drałus

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p11.pdf

Abstract: Decomposition methods for solving large-scale Traveling Salesman Problem (TSP) are presented. Three approaches are proposed: macromodeling for clustered TSP as well as extending and “ring” methods for arbitrary points’ distribution. Four stages of the problem solving include partitioning of the input set of points into small subsets, finding the partial high quality solutions in the subsets, merging the partial solutions into the complete initial solution and optimizing the final solution. Experimental investigations as well as the comparative analysis of the results and their effectiveness estimation in terms of quality and running time were conducted. The suggested approaches provide substantial reduction in the running time in comparison with the existing heuristic algorithms. The quality loss is small. The problem instances up to 200,000 points were investigated. The TSP is extensively applied in transportation systems analysis, printed circuit boards, VLSI, SoC and NoC computer-aided design, testing and manufacturing, laser cutting of plastics and metals, protein structure research, continuous line drawings, X-ray crystallography as well as in number of other fields.

Keywords: traveling salesman problem, combinatorial NP-hard problems, decomposition, large-scale.

ACM Classification Keywords: G.2.1 Combinatorics - Combinatorial algorithms; I.2.8 Problem Solving, Control Methods, and Search - Heuristic methods.

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DECOMPOSITION METHODS FOR LARGE-SCALE TSP

Roman Bazylevych, Marek Pałasiński, Roman Kutelmakh, Bohdan Kuz, Lubov Bazylevych

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p10.pdf

Abstract: The aim of the paper is to present the assumptions and the architecture of the system for searching similarity in string sets. During the research all the required steps of a procedure of text documents processing which includes text extraction, pruning, stemming and lemmatization were analysed. Models of a text documents’ description and the method of creating a vector of features were developed as well. This vector consists, inter alia, of chosen words and the number of their occurrences. The process of the text analysis is supported by a set of various dictionaries. These are Stop-words, Domain and Lemma dictionaries and all of them were considered in the context of the Polish language. Because the Lemma dictionary is supposed to consist of many entries, the efficient method of its access optimisation was elaborated. Various measures used for calculating degree of a text documents similarity were studied too. Moreover, the method for determining the quality of user queries and text documents adjustment were proposed. The system was realized in accordance with the idea of multi-agent systems. Its functionality is ensured by the set of agents acting on the basis of separate threads. In the research, tests of the system work efficiency were also performed.

Keywords: agent systems, text similarity search

ACM Classification Keywords: I.7 Document And Text Processing

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MULTI-AGENT SYSTEM FOR SIMILARITY SEARCH IN STRING SETS

Katarzyna Harężlak, Michał Sala

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p09.pdf

Abstract: The main goal of the research that preliminary results have been presented in this paper is to develop an agent–oriented electronic marketplace for modeling and simulation of dynamic pricing models, i.e. models in which the price of the item is allowed to fluctuate as supply and demand in a market change. The work provides an overview of forms of dynamic pricing models, with particular emphasis on auctions. After that, the main rationale behind using Multi-agent Systems approach for modeling and simulation of complex business structures has been shown. Then the development process of an electronic marketplace, including agents’ architecture as well as implementation environment selection, structure and business logic of e-marketplace have been presented. Last part of the paper comprises conclusions and further research plans.

Keywords: Agent-Based? Models, Simulation for MAS development, Agent-oriented Marketplace Design, Multi Agent Based Modeling and Simulation, Dynamic Pricing Models

ACM Classification Keywords: I. Computing Methodologies; I.2 Artificial Intelligence; I.2.11 Distributed Artificial Intelligence; Multi-Agent? Systems

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AN AGENT–ORIENTED ELECTRONIC MARKETPLACE FOR MODELING AND SIMULATION OF DYNAMIC PRICING MODELS BUSINESS LOGIC

Jacek Jakieła, Paweł Litwin, Marcin Olech

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p08.pdf

Abstract: The developed technology consists of three main stages: collection of information from a search engine; sifting irrelevant information by the pre-selected features; ranking the obtained results by relevance to a user's request. The ranking model is built with the usage of inductive1 GMDH algorithms. The article describes the effectiveness investigation of the developed technology improving the search relevance of target information on the Internet compared with the Google search engines. When studying, three experiments were conducted with one search request chosen for each experiment. The search results for every request obtained from Google were subsequently processed with the developed technology. The first 100 sites from Google SERP were analyzed to compare the relevance level of Google search and that provided with our technology. Outcomes of the experiments are given in the form of circle diagrams showing the percentage of different types of sites in the search results before and after processing it using the proposed technology. The research demonstrates higher effectiveness of the proposed technology compared to Google search: the developed technology allows achieving the search relevance at the level of 80%. Application of this technology will enable more convenient and relevant search of target information on the Internet.

Keywords: Information search, target information, search engine, search relevance, inductive modeling.

ACM Classification Keywords: H.3.5 Information Search and Retrieval - Information Filtering; H.3.5 Online Information Services – Web-based Services

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ENHANCED TECHNOLOGY OF EFFICIENT INTERNET RETRIEVAL FOR RELEVANT INFORMATION USING INDUCTIVE PROCESSING OF SEARCH RESULTS

Vyacheslav Zosimov, Volodymyr Stepashko, Oleksandra Bulgakova

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p07.pdf

Abstract: NL-addressing is a possibility to access information using natural language words as addresses of the information stored in the multi-dimensional numbered information spaces. For this purpose the internal encoding of the letters is used to generate corresponded co-ordinates. The tool for working in such style is named OntoArM?. Its main principles, functions and using for storing RDF graphs are outlined in this paper.

Keywords: NL-addressing, RDF graphs, ontology representations.

ACM Classification Keywords: D.4.2 Storage Management; E.2 Data Storage Representations.

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STORING RDF GRAPHS USING NL-ADDRESSING

Krassimira Ivanova, Vitalii Velychko, Krassimir Markov

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p06.pdf

Abstract: We describe our approach to building the model of the search in libraries’ catalogues based on the fuzzy similarity relation. To construct the model we carried out an experimental search to the variants of name in the catalogs of two libraries - the German National Library and the National Library of France. The model we constructed is based on the result of our experiment.

Keywords: fuzzy similarity relation, names transliteration, authority control, library catalogs

ACM Classification Keywords: H.3.6 Library Automation H.3.3, Information Search and Retrieval, 1.5.1 Pattern recognition. Fuzzy set

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BUILDING THE LIBRARY CATALOG SEARCH MODEL BASED ON THE FUZZY SIMILARITY RELATION

Liliya Vershinina, Mikhail Vershinin, Andrej Masevich

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p05.pdf

Abstract: RUBRYX is a document classifier developed in 2000s for processing large volumes of Web information. RUBRYX uses weighted sum of n-grams (n=1,2,3) extracted from a very limited number of samples (about 5-10) and takes into account their mutual position in a given text. This sophisticated algorithm proves to be very effective in classifying primary medical records presented in a free text form. In the paper we study possibilities of RUBRYX (version 2.2) on a limited document set in Spanish. These documents are medical histories related to stomach diseases. Such area should be considered as a narrow subset of medical records. The high quality of archived results (accuracy 80%-90%) allows us to recommend RUBRYX for similar applications.

Keywords: natural language processing, medical diagnostics, document classification

ACM Classification Keywords: I.2.7 Natural Language Processing

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CLASSIFICATION OF PRIMARY MEDICAL RECORDS WITH RUBRYX-2: FIRST EXPERIENCE

Olga Kaurova, Mikhail Alexandrov, Ales Bourek

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p04.pdf

Abstract: Machine translation now left laboratories and became one of the practices of information service. A large number of free or partly free systems of machine translation became available in the net. Consequently, the task of their comparison and evaluation criteria arises. The paper describes the procedure of the estimation of the machine translation of text executed by different machine translation systems. The systems did translation into the Russian language of text (fragment “Communist manifesto” by Marx and Engels) from German, English and French. Students of the philological department of Saint-Petersburg? State University systematized errors of translation, tried to determine the sources of errors, and considered the possibility (or impossibility) of their elimination.

Keywords: machine translation, evaluation, teaching

ACM Classification Keywords: I.2.7 Natural Language Processing

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MACHINE TRANSLATION IN THE COURSE “COMPUTER TECHNOLOGIES IN LINGUISTICS” AT THE PHILOLOGICAL DEPARTMENT OF THE SAINT-PETERSBURG UNIVERSITY

Andrei Masevich, Victor Zakharov

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p03.pdf

Abstract: Social networking tools, blogs and microblogs, user-generated content sites, discussion groups, problem reporting, and other social services have transformed the way people communicate and consume information. Yet managing this information is still a very onerous activity for both the consumer and the provider, the information itself remains passive. Traditional methods of keyword extraction from text based on predefined codified knowledge are not well suited for use in such empirical environments, and as such do little to support making this information more an active part of the processes to which it may otherwise belong. In this paper we analyse various use cases of real-time context-sensitive keyword detection methods using IBM LanguageWare? applications as example. We present a general high-performance method for exploiting ontologies to automatically generate semantic metadata for text assets, and demonstrate examples of how this method can be implemented to bring commercial and social benefits. In particular, we overview metadata-driven semantic publishing on the BBC FIFA World Cup 2010 website and the applications for social semantic desktops.

Keywords:data mining, natural language processing, recommender systems, social semantic web, graph-based methods.

ACM Classification Keywords: H.3.4 Information Storage and Retrieval: Systems and Software – information networks; H.3.5 Information Storage and Retrieval: Online Information Services – data sharing.

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APPLICATION OF SOCIAL ANALYTICS FOR BUSINESS INFORMATION SYSTEMS

Alexander Troussov, D.J. McCloskey?

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p02.pdf

Abstract: The problem for existence and determination of the set of positive asymptotically stable realizations of a proper transfer function of linear continuous-time systems is formulated and solved. Necessary and sufficient conditions for existence of the set of the realizations are established. Procedure for computation of the set of realizations are proposed and illustrated by numerical examples.

Keywords: positive, stable, realization, existence, procedure, linear, continuous-time, system.

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POSITIVE STABLE REALIZATIONS OF CONTINUOUS-TIME LINEAR SYSTEMS

Tadeusz Kaczorek

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p01.pdf

Abstract: The authors present a hardware implementation of the codec for rank codes. Parameters of rank code are (8,4,5). Algorithmwas implemented on FPGA Spartan 3. Code rate is 1/2. The codec operates with elements from Galois field GF(28). The device can process informational data stream up to 77 MiB/s. Proposed results should help understanding rank code structure and simplify the problemof its application.

Keywords: rank codes, codec, error correction code, weak self-orthogonal bases, rankmetric, FPGA, key equation, Euclidean algorithm, fast computing.

ACMClassification Keywords: B.2.4 High-Speed? Arithmetic

MSC: 12Y05, 65Y20, 68W35.

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HARDWARE IMPLEMENTATION OF RANK CODEC

Igor Sysoev,Ernst Gabidulin

http://www.foibg.com/ibs_isc/ibs-25/ibs-25-p19.pdf

Abstract: The mechanism by which neuronal networks dynamically organize and differentiate during development is a salient issue concerning neurogenesis. This central event reflects a topological order and its metrizability. One important parameter in this process concerns the role of tremor and intrinsic electrical properties of neurons Llinàs 1988 from a different in the developmental organization of Central Nervous System (CNS), which we now would like to develop more formally. While tremor is usually considered an undesirable parameter in the generation of coordinated movement it is actually essential in efficient motor execution and reflects fundamental intrinsic neuronal electrophysiological oscillation. In addition, we propose, such intrinsic properties contribute to organize the neuronal connectivity that results in the development of internal coordinate reference systems. Thus the degree of coherence in the oscillatory activities of neuron can be interpreted as embodying a metric tensor of non-Euclidean space that produce topological order associated to CNS development.

Keywords: Degree of coherence, Metric tensor, Nervous System, Intrinsic Oscillation, Functional Geometry

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METRIC TENSOR AS DEGREE OF COHERENCE IN THE DYNAMICAL ORGANIZATION OF THE CENTRAL NERVOUS SYSTEM

Sisir Roy, Rodolfo Llinás

http://www.foibg.com/ibs_isc/ibs-25/ibs-25-p18.pdf