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Intelligent Systems and Informatics, 2008. SISY 2008. 6th International Symposium on

Date 26-27 Sept. 2008

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Displaying Results 1 - 25 of 88
  • Committees

    Publication Year: 2008 , Page(s): 1
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  • [Copyright notice]

    Publication Year: 2008 , Page(s): 1
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  • General information

    Publication Year: 2008 , Page(s): 1
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  • Table of contents

    Publication Year: 2008 , Page(s): 1 - 5
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  • [Title page]

    Publication Year: 2008 , Page(s): 1
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  • Welcome message

    Publication Year: 2008 , Page(s): 1
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  • Copulas: A tool for modeling the dependence structure of random vectors

    Publication Year: 2008 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (131 KB) |  | HTML iconHTML  

    Copulas enabling to characterize the joint distributions of random vectors by means of the corresponding one-dimensional marginals are presented and discussed. Some properties of copulas and several construction methods, especially when a partial knowledge is available, are included. Possible applications are indicated. View full abstract»

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  • Dynamic balance concept and the maintenance of the dynamic balance in humanoid robotics

    Publication Year: 2008 , Page(s): 1 - 11
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (653 KB) |  | HTML iconHTML  

    One of basic characteristics of regular bipedal walk of humanoid robots is the maintenance of their dynamic balance during the walk, whereby a decisive role is played by the unpowered degrees of freedom arising at the foot-ground contact. Hence, the role of Zero-Moment Point (ZMP) as an indicator of dynamic balance is indispensable. The paper gives a very detailed discussion of some basic theoretical assumptions related to the notion of dynamic balance (term stability, is often used as a synonym, which we consider erroneous). Some special cases of gait in which dynamic balance need not be realized are also discussed. View full abstract»

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  • ”Aggregation Functions”, Cambridge University Press

    Publication Year: 2008 , Page(s): 1 - 7
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (123 KB) |  | HTML iconHTML  

    There is given a short overview of the monograph rdquoAggregation Functionsrdquo (M. Grabisch, J. L. Marichal, R. Mesiar, E. Pap), Cambridge University Press (in press). View full abstract»

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  • Solution for an invoice system used in the field of optometry

    Publication Year: 2008 , Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (566 KB) |  | HTML iconHTML  

    This paper presents the design, development and implementation of the database and application for the book-keeping of goods of a small trade. The goal is to portray the invoice system and the solutions for the practical conveniences with the emphasis on the customerspsila view on it. The application is created specially to meet the needs of an optometrist and his workshop. The following aspects are included in this article: defining the goods, giving offers, creating the invoice and finally, the methods of presenting the invoice to the customer and the book-keeper. The majority of the data input is automated. The application is written in VB6 using MSAccess for the database. The complete invoice can be printed and exported to PDF or XML format, which can be sent via email and is easily processed by any other application. View full abstract»

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  • Learning automata-based co-evolutionary genetic algorithms for function optimization

    Publication Year: 2008 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (256 KB) |  | HTML iconHTML  

    Co-evolutionary genetic algorithms are being used to solve the problems which are naturally distributed and need the composition of couple of elements or partial solutions to be solved. In these algorithms, the problem decomposes into several elements and for each element, a sub-population is regarded. These sub-populations evolve separately by considering the way of interactions among them. The general solution is the result of composition of some individuals from the mentioned sub-populations. These algorithms are more similar to the natural evolution and can be run in parallel and therefore, are more efficient. Function optimization problem is one of the examples of distributed problems in which co-evolutionary genetic algorithms can be used appropriately. To solve the problem, at the first step, we should know whether the variables of the problem are dependent or not. In each case, a different approach should be taken. But, sometimes the recognition of variable dependencies is too hard because of the complexity or discreteness of the functions. This paper represents a solution using a combination of co-evolutionary genetic algorithm and learning automata to address this problem. Learning automata are able to learn the dependence (or independence) of variables and choose the appropriate approach for each case. Experimental results show that using learning automata improves the efficiency of co-evolutionary algorithms and make them suitable for the optimization of any function. View full abstract»

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  • Lazy multigram learning environment for automatic content recommendation systems

    Publication Year: 2008 , Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (208 KB) |  | HTML iconHTML  

    In this paper I present an environment and algorithm for lazy (incremental) construction of multigram profile models as part of IR (information retrieval) training and exploitation processes. N-grams are traditionally used for natural language text models, but they can be also successfully used for domain independent document classification. I am demonstrating results of a prototype utility which proves these ideas. View full abstract»

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  • Sensor-data-fusion for an autonomous vehicle using a Kalman-filter

    Publication Year: 2008 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (534 KB) |  | HTML iconHTML  

    This paper presents a method to estimate the system-state, especially the full position, of an autonomous vehicle using sensor data fusion of redundant position signals based on an extended Kalman-filter. The position is detected with the help of magnet sensors attached at the vehicle and a global camera signal with low resolution, similar to GPS. A lane marked with permanent magnets and an infrared camera are used for this purpose. The vehiclepsilas driving dynamics are described using a nonlinear single-track model. View full abstract»

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  • The Hough transformation of rectangle

    Publication Year: 2008 , Page(s): 1 - 4
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB) |  | HTML iconHTML  

    Hough transformation for finding straight line segments is well known in digital image processing. In this paper we have used Hough, houghline and houghpeaks functions for creating a new 4- dimensional parameter space named h_space for extracting rectangles in digital image. These parameters are coordinates of the center of a rectangle (intersection of diagonals), angle that the shorter edge makes with x-axis and length of the longer edge. In our opinion this result might be useful for the shape descriptors associated with rectangles because the parameters of h-space are suitable for calculating descriptors of a rectangle (like area and perimeter) and they give a good idea about the position of a rectangle. View full abstract»

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  • Statistical method for determination of interspike interval probability density function

    Publication Year: 2008 , Page(s): 1 - 5
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (403 KB) |  | HTML iconHTML  

    Assuming homogenity of the underlying statistics, interspike interval probability density functions recorded from behaving macaque monkeys are determined with a special statistical fitting method. The nature of the problem needed robust statistics approach, the results were validated with the Kolmogorov-Smirnov test. The results suggest that there is no single/universal interspike interval probability density function, but many, and the underlying statistics is definitely not Poissonian. View full abstract»

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  • Tractatus on the knowledge approximation

    Publication Year: 2008 , Page(s): 1 - 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (92 KB) |  | HTML iconHTML  

    The idea of knowledge approximation is naturally rooted in the usual examples of approximation in our experience. More abstract approximation, at the levels of syntactic structures representing knowledge, is becoming a need in the existing knowledge management, addressing a variety of important issues. Here we propose a method and its implementation, which generalizes the unification method to the syntax components for which the classic unification is not performed, up to the second order level (variables for function and predicate symbols allowed). In this way we can naturally talk on the knowledge approximation and similar concepts dispersed in the broad class of contexts. View full abstract»

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  • Solving Multi-Agent Markov Decision Processes using learning automata

    Publication Year: 2008 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (257 KB) |  | HTML iconHTML  

    Multi-agent Markov decision processes (MMDPs) are widely used for modeling many types of multi-agent systems. In this paper, two new algorithms based on learning automata are proposed for solving MMDPs and finding optimal policies. In the proposed algorithms, Markov problem is described as a directed graph. The nodes of this graph are the states of the problem, and the directed edges represent the actions that result in transition from one state to another. Each node in the graph is equipped with a learning automaton and its actions are the outgoing edges of that node. Each agent moves from one node to another and tries to reach the goal state. In each node, the agent chooses its next transition with help of the learning automaton in that node. The actions taken by learning automata along the path traveled by the agent is then rewarded or penalized based on the cost of the traveled path according to a learning algorithm. This way the optimal policy for the agent will be gradually reached. The results of experiments have shown that our proposed algorithms perform better than the existing learning automata based algorithms in terms of cost and the speed of reaching the optimal policy. View full abstract»

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  • Document space dimension reduction by Latent Semantic Analysis and Hebbian neural network

    Publication Year: 2008 , Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (184 KB) |  | HTML iconHTML  

    This paper presents the comparison of the text document space dimension reduction and the text document clustering and also the keyword space dimension reduction and keyword clustering by the latent semantic analysis and by the Hebbian neural network with Oja learning rule. Results of this neural network are compared with the results of the latent semantic analysis which uses the Singular value decomposition for dimension space reduction of the text documents in natural language. View full abstract»

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  • Dependency-based mapping between symbolic language and Extended Conceptual Graph

    Publication Year: 2008 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (458 KB) |  | HTML iconHTML  

    The goal of our research is to develop a grammar induction system that can assign descriptive sentences to ontology models represented by an extended conceptual graph (ECG) which is a conceptual modeling language for describing the semantics of an agentpsilas internal knowledge model. In the proposed system, the ECG model of the agent is converted into a symbolic language sentence. For this, the system should learn first the rules of association between sentence elements and ECG model elements. In order to learn the grammar rules, the system should be presented with training sentences carrying information on the ordering and inflection of words. This paper presents the training algorithm of the grammar induction system in details. View full abstract»

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  • DSP platform for 64-channel brain cell signal preprocessing

    Publication Year: 2008 , Page(s): 1 - 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (227 KB) |  | HTML iconHTML  

    The problem of correct radio telemetry can only be solved if the signal is preprocessed. This work presents the pre-distortion and reconstruction process of a real time signal, and its effects. View full abstract»

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  • Algorithm model for LDPC matrix loading—application to a more efficient analysis of error correction codes

    Publication Year: 2008 , Page(s): 1 - 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (153 KB) |  | HTML iconHTML  

    The paper describes an algorithm model for loading low density parity check (LDPC) matrices. The paperpsilas main purpose is to present an algorithm solution which enables LDPC matrices to be loaded, providing more rapid and efficient application of analysis of error correction codes. View full abstract»

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  • Threshold potential optimization in the Pulse Coupled Neural Network

    Publication Year: 2008 , Page(s): 1 - 4
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (233 KB) |  | HTML iconHTML  

    The paper introduces an approach for threshold potential optimization in the Pulse Coupled Neural Network (PCNN) to solve the quality of generated features for image recognition tasks. Threshold potential plays very important role because it provides and controls pulse effect of PCNN. The suitable control of pulse effect can improve dimension reduction of image classification space by PCNN that is necessary for successful image recognition with high input image dimension. View full abstract»

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  • A dominator path scheduler for deep pipeline architectures

    Publication Year: 2008 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (156 KB) |  | HTML iconHTML  

    There are many different instruction scheduling techniques that are used in compilers today. The most frequently used one is the list scheduling. It is easy to implement and gives good results in most cases. It works on the basic block level. According to Fisher and Rau instruction level parallelism that can be achieved using this technique cannot be greater then 2. Architectures with deep pipeline can run much more instructions in the same cycle and global scheduler techniques can give better results. In our tests we used a dominator path scheduler with three different strategies for choosing the scheduling paths. Our application encoded sound to the mp3 format, and was run on a digital signal processor with a deep pipeline. Using these scheduling techniques, our C compiler generated 10% fewer NOP instructions compared to local instruction scheduling. In addition, this implementation of a dominator path scheduler was used on a C compiler for SUN Sparc processors (for testing purposes only), and it was shown that the same implementation of the scheduler can be used for different platforms. View full abstract»

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  • Content-based image retrieval

    Publication Year: 2008 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (596 KB) |  | HTML iconHTML  

    A picture is worth a thousand words. Yes, but which ones? Content-based image retrieval (CBIR) is the application of computer vision to the image retrieval problem. The image retrieval problem is the problem of searching for digital images in large databases. ldquoContent-basedrdquo means that the search will analyze the actual content of the image. The term dasiacontentpsila in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Without the ability to examine image content, search must rely on metadata such as captions or keywords, which may be laborious or expensive to produce. How this problem is used in real world examples? Can algorithms really guess the similarity which is subjective category? Can similarity works without the context? How this reflects the consumer approach to online services? The presented paper gives some ideas that should clarify the raised questions. View full abstract»

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  • On image compression systems for band-limited information networks

    Publication Year: 2008 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (400 KB) |  | HTML iconHTML  

    A new approach to rate-control of visual communication is presented. This method is based on a recently introduced rate-distortion function for image compression systems. We show that this rate-distortion approach can be used to develop an efficient coding algorithm, suitable for wireless and Internet transmission. We demonstrate this approach for subband transform coding using the discrete cosine transform (DCT). We show how rate-control is performed both for still images and for video streams. Simulation results are presented to support the efficiency of our algorithm, which outperforms presently available compression methods. View full abstract»

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