Abstract:Telecommunication connections are highly reliable and manageable, however, the handling of several parts of the networks is problematic. One of these parts is the access network. The variegation of the applied technologies and the individual connections to the customers in access networks makes the preliminary estimation of the performance of the telecommunications services and troubleshooting difficult. There are existing methods which can handle such problems, but the telecommunications companies (TELCO) are continuously looking for newer and more efficient methods. In this paper some existing methods for performance evaluation and the prediction of the probable failures of the wire pairs of telecommunications access networks are reviewed and novel methods that are based on the measurements of the wire pairs and use computational intelligence, fuzzy inference methods and evolutionary models are introduced.
Abstract:This paper shows practical examples of compound object comparators and the application of the theory in various fields related to data processing systems. One can also find the necessary theoretical background needed to understand the examples.
Abstract:The paper summarizes research on the cryptographic strength of a new symmetric block cipher based on the Feistel network. The classification of cryptographic attacks, depending on the cryptanalyst’s input data, is considered. For the purpose of testing, the linear and differential cryptanalysis as well as the Slide attack were used.
Abstract:The paper investigates the possibility of using soft computing for estimating the value of social capital. Our approach is applied to the case of Red Hat Inc. – the world’s leading provider of open source solutions. The objective of the research was to develop an artificial neural network for forecasting the value of social capital. These studies also allow us to identify variables significantly affecting the value of social capital. Computer simulations and assessments were done using software package STATISTICA Automated Neural Networks. The paper concludes with discussion and proposals for further research.
Abstract:Images and video are often coded using block-based discrete cosine transform (DCT) or discrete wavelet transform (DWT) which cause a great deal of visual distortions. Restoration of image sequences can obtain better results compared to restoring each image individually, provided that the temporal redundancy is adequately used. In this article, efficient approach for artifacts reduction has been presented. In order to enhance the overall video quality, the proposed approach uses image sequence redundancy. Spatial and temporal information is used for the video de-noising process.
Abstract:This paper deals with European share option pricing using quantum physics methods. These contingent claims are usually priced using the Black-Scholes equation. This nonlinear parabolic equation is based on geometric Brownian motion model of the stock price stochastic process. Similar processes also appear among quantum particles and are described by the time-dependent Schrödinger equation. In this paper, the option pricing based on the Schrödinger equation approach is proposed. Using Wick transformation, the Black-Scholes equation is transformed into the equivalent Schrödinger equation. The Fourier separation method is used to find analytical solutions to this equation. The last square method is used to calibrate the Schrödinger model based on real market data. Numerical results are provided and discussed.
Abstract:In this paper, the latest member of the FUzzy-BAsed character Recognizer (FUBAR) algorithm family with multi-stroke character support is presented. The paper summarizes the basic concept and development of multi-stroke FUBAR and compares the single-stroke, multi-stroke FUBAR algorithms with the most similar methods found in literature.
Donis-Diaz, Carlos A., Bello, Rafael, Kacprzyk, Janusz
Abstract:This paper presents work is presented an enhanced Genetic Algorithm (GA) specifically designed for the production of linguistic data summaries. The model is able to obtain not a set of ‘good linguistic summaries’ but a ‘good set’ of summaries. The model incorporates an operator and fitness function specially designed to fulfil this aim. Experiments show how the enhanced model is able to improve results obtained with the classical model of GA and to guarantee a summary with high diversity and good values for the quality measures in individual summaries.