Informatica

The aim of the given paper is the development of an approach for parametric identification of Hammerstein systems with piecewise linear nonlinearities, i.e., when the saturation-like function with unknown slopes is followed by a linear part with unknown parameters. It is shown here that by a simple input data rearrangement and by a following data partition the problem of identification of a nonlinear Hammerstein system could be reduced to the linear parametric estimation problem. Afterwards, estimates of the unknown parameters of linear regression models are calculated by processing respective particles of input-output data. A technique based on ordinary least squares is proposed here for the estimation of parameters of linear and nonlinear parts of the Hammerstein system, including the unknown threshold of the piecewise nonlinearity, too. The results of numerical simulation and identification obtained by processing observations of input-output signals of a discrete-time Hammerstein system with a piecewise nonlinearity with positive slopes by computer are given.

The asynchronous techniques that exist within the programming with distributed constraints are characterized by the occurrence of the nogood values during the search for the solution. The nogood type messages are sent among the agents with the purpose of realizing an intelligent backtrack and of ensuring the algorithm's completion.

This paper presents an application of the Hilbert–Huang transform (HHT) and ensemble correlation for detection of the transient evoked otoacoustic emissions (TEOAEs), and high resolution time–frequency mapping. The HHT provides a powerful tool for nonlinear analysis of nonstationary signals such as TEOAEs. Since the HHT itself does not distinguish between signal and noise it was used with ensemble correlation to extract information about intervals with correlated activity. The combination of methods produced good results for both tasks TEOAE detection and time–frequency mapping. The resulting detection performance, using the mean hearing threshold as audiological separation criterion, was a specificity of 81% at a sensitivity of 90% to be compared to 65% with the traditional wave reproducibility detection criterion. High resolution time frequency mapping predicted in more than 70% of the cases hearing loss at a specific frequency in cases of ski-sloping audiograms. The present m ethod does not require a priori information on the signal and may, with minor changes, be successfully applied to analysis of other types of repetitive signals such as evoked potentials.

We study single machine scheduling problems, where processing times of the jobs are exponential functions of their start times. For increasing functions, we prove strong NP-hardness of the makespan minimization problem with arbitrary job release times. For decreasing functions, maximum lateness minimization problem is proved to be strongly NP-hard and total weighted completion time minimization problem is proved to be ordinary NP-hard. Heuristic algorithms are presented and computationally tested for these problems.

Localization is a complex process based on translation and adaptation of software features. Usually localization progress is identified with the number of translated resource strings. The paper investigates the dependency of number of translated strings to amount of human resources used. It is shown that the number of translated strings increases much slower at the end of the work than at beginning. The last strings are especially difficult to translate. Quantitative evaluation of dependency between number of strings in progress and human resources is presented.

Vector Product Format (VPF) based databases store geographical data in a relational framework, where individual VPF files are arranged hierarchically in a directory tree structure. Access and update of the VPF data can become difficult due to fragmentation of data among multiple tables. This paper presents an object-oriented model for the management of a VPF database, which provides easy access and automatic update for the VPF data, and is compatible with ESRI ArcView. This model has been successfully implemented in Java, for Digital Nautical Charts (DNC).

T wave features suitable for automatic T wave alternans detection in low signal-to-noise ratio electrocardiograms are explored using a correlation-to-template-based algorithm for detecting T waves of variable duration. Amplitude and area features of T waves are found to be notably less sensitive to template selection than are duration features. T wave alternans features and measures which can be determined more stably provide better classification accuracy of patients with and without coronary artery lesions.

Due to high nonlinearities and time-varying dynamics of today's control systems fuzzy learning controllers find appliance in practice. The present paper proposes a method for the synthesis of the learning fuzzy controllers where an expert knowledge about a process is applied to form a learning mechanism that is used to acquire information for the knowledge base of the main fuzzy controller. According to the proposed method an expert knowledge is used to describe how the controller should learn to control rather than to control the process. The results of experiments on heating system and level/pressure system prove the practical relevance of the design strategy of a learning fuzzy controller.

The Star Plot approach to high-dimensional data visualization is applied to multi-attribute dichotomies. It is observed that the areas of the plot for the two parts of a dichotomy may be used as an aggregate measure of their relative dominance. An optimization model is developed to determine a topology (or weighted configuration of the attributes) that maximizes the resolution of this measure with respect to a given set of reference dichotomies.

This paper presents a new approach for human cataract automatical detection based on ultrasound signal processing. Two signal decomposition techniques, empirical mode decomposition and discrete wavelet transform are used in the presented method. Performance comparison of these two decomposition methods when applied to this specific ultrasound signal is given. Described method includes ultrasonic signal decomposition to enhance signal specific features and increase signal to noise ratio with the following decision rules based on adaptive thresholding. The resulting detection performance of the proposed method using empirical mode decomposition was better to compare to discrete wavelet transform and resulted in 70% correctly identified “healthy subject” cases and 82%, 97% and 100% correctly identified “cataract cases” in the incipience, immature and mature cataract subject groups, respectively. Discussion is given on the reasons of different results and the differences between the two used signal decomposition techniques.