Informatica

This paper deals with maximum likelihood and least square segmentation of autoregressive random sequences with abruptly changing parameters. Conditional distribution of the observations has been derived. Objective function was modified to the form suitable to apply dynamic programming method for its optimization. Expressions of Bellman functions for this case were obtained. Performance of presented approach is illustrated with simulation examples and segmentation of speech signals examples.

Fingerprint ridge frequency is a global feature, which is most prominently different in fingerprints of men and woman, and it also changes within the maturing period of a person. This paper proposes the method of fingerprint pre-classification, based on the ridge frequency replacement by the density of edge points of the ridge boundary. This method is to be used after applying the common steps in most fingerprint matching algorithms, namely the fingerprint image filtering, binarization and marking of good/bad image areas. The experimental performance evaluation of fingerprint pre-classification is presented. We have found that fingerprint pre-classification using the fingerprint ridge edges density is possible, and it enables to preliminary reject part of the fingerprints without heavy loss of the recognition quality. The paper presents the evaluation of two sources of fingerprint ridge edges density variability: a) different finger pressure during the fingerprint scanning, b) different distance between the geometrical center of the fingerprint and position of the fingerprint fragment.

The paper presents an intelligent GIS architecture that enables us to extend GIS functionality by using domain specific knowledge and inference engine. In this architecture, an intelligent agent monitors events, which occur in the GIS environment, and execute tasks depending on user's actions. The intelligent agent includes an expert system shell and knowledge base. A hybrid knowledge representation method is used that integrates rule-based, object-oriented, and procedural knowledge representations.

In this paper we present an algorithm for generating quadratic assignment problem (QAP) instances with known provably optimal solution. The flow matrix of such instances is constructed from the matrices corresponding to special graphs whose size may reach the dimension of the problem. In this respect, the algorithm generalizes some existing algorithms based on the iterative selection of triangles only. The set of instances which can be produced by the algorithm is NP-hard. Using multi-start descent heuristic for the QAP, we compare experimentally such test cases against those created by several existing generators and against Nugent-type problems from the QAPLIB as well.

In the previous paper (Pupeikis, 2000) the problem of closed-loop robust identification using the direct approach in the presence of outliers in observations have been considered. The aim of the given paper is a development of the indirect approach used for the estimation of parameters of a closed-loop discrete-time dynamic system in the case of additive correlated noise with outliers contaminated uniformly in it. To calculate current M-estimates of unknown parameters of such a system by means of processing input and noisy output observations, obtained from closed-loop experiments, the recursive robust technique based on an ordinary recursive least square (RLS) algorithm is applied here. The results of numerical simulation of closed-loop system (Fig. 3) by computer (Figs. 4–7) are given.

The paper presents new method for sequential classification of the time series observations. Methods and algorithms of sequential recognition are obtained on the basis of the recursive equations for sufficient statistics. These recursive equations allow to construct algorithms of current classification of observable sequences in the rate of entering its values into the on-line operation. Classification algorithms are realized in the form of computer programs, including personal computers. They allow to build multi-channel conveyer computational structures for the sequential recognizers of time series observations.

In this paper we suggest a three-language (3L) paradigm for building the program generator models. The basis of the paradigm is a relationship model of the specification, scripting and target languages. It is not necessary that all three languages would be the separate ones. We consider some internal relationship (roles) between the capabilities of a given language for specifying, scripting (gluing) and describing the domain functionality. We also assume that a target language is basic. We introduce domain architecture (functionality) with the generic components usually composed using the scripting and target languages. The specification language is for describing user's needs for the domain functionality to be extracted from the system. We present the framework for implementing the 3L paradigm and some results from the experimental systems developed for a validation of the approach.