Band 7, Heft 1

Parameterization of the speech signal using the algorithms of analysis synchronized with the pitch frequency is discussed. Speech parameterization is performed by the average number of zero transitions function and the signal energy function. Parameterization results are used to segment the speech signal and to isolate the segments with stable spectral characteristics. Segmentation results can be used to generate a digital voice pattern of a person or be applied in the automatic speech recognition. Stages needed for continuous speech segmentation are described.

One of the most important steps in recognizing fingerprint is accurate feature extraction of the input image. To enhance the accuracy of fingerprint recognition, an algorithm using fractional derivatives is proposed in this paper. The proposed algorithm uses the definitions of fractional derivatives Riemann-Liouville (R-L) and Grunwald-Letnikov (G-L) in two sections of direction estimation and image enhancement for the first time. Based on it, new mask of fractional derivative Gabor filter is calculated. The proposed fractional derivative-based method enhances the image quality. This method enhances the structure of ridges and grooves of fingerprint, using fractional derivatives. The efficiency of the proposed method is studied in images of FVC2004 (DB1, DB2, DB3 and DB4) database and the results are evaluated using the criteria including entropy, average gradient, and edge intensity. Also, performance of the proposed method is compared with other technical methods such as Gabor filter. Based on the obtained results from the tests, the method is able to enhance the quality of fingerprint images significantly.

Let B n = {x i · x j = x k : i, j, k ∈ {1, . . . , n}} ∪ {x i + 1 = x k : i, k ∈ {1, . . . , n}} denote the system of equations in the variables x 1 , . . . , x n . For a positive integer n, let _(n) denote the smallest positive integer b such that for each system of equations S ⊆ B n with a unique solution in positive integers x 1 , . . . , x n , this solution belongs to [1, b]n. Let g(1) = 1, and let g(n + 1) = 2 2g(n) for every positive integer n. We conjecture that ξ (n) 6 g(2n) for every positive integer n. We prove: (1) the function ξ : N \ {0} → N \ {0} is computable in the limit; (2) if a function f : N \ {0} → N \ {0} has a single-fold Diophantine representation, then there exists a positive integer m such that f (n) < ξ (n) for every integer n > m; (3) the conjecture implies that there exists an algorithm which takes as input a Diophantine equation D(x 1 , . . . , x p ) = 0 and returns a positive integer d with the following property: for every positive integers a 1 , . . . , a p , if the tuple (a 1 , . . . , a p ) solely solves the equation D(x 1 , . . . , x p ) = 0 in positive integers, then a 1 , . . . , a p 6 d; (4) the conjecture implies that if a set M ⊆ N has a single-fold Diophantine representation, then M is computable; (5) for every integer n > 9, the inequality ξ (n) < (2 2n−5 − 1) 2n−5 + 1 implies that 2 2n−5 + 1 is composite.

In this work we present the algorithms to produce deterministic finite automaton (DFA) for extended operators in regular expressions like intersection, subtraction and complement. The method like “overriding” of the source NFA(NFA not defined) with subset construction rules is used. The past work described only the algorithm for AND-operator (or intersection of regular languages); in this paper the construction for the MINUS-operator (and complement) is shown.

In this article a software implementation of the environmental monitoring is developed and presented, which is responsible for receive, store, process and analysis of data. For logical database design system Computer- Aided Software Engineering (CASE) technology, the AllFusion ERwin Data Modeler was selected. To develop corporate Oracle database management system used. The database contains a set of objects, which store all the primary and additional service information, as well as a set of software modules of business logic. The developed information system makes it possible to find optimal solutions for clean and disposal of the contaminated areas. There are advantages of created databases on the areas to be remediated, such as the analysis of remediation made by using plants.

The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD) unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA) machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

Fuzzy set theory has been applied in many fields such as operations research, control theory and decision sciences. In particular, an application of this theory in decision making problems has a remarkable significance. In this paper, we consider a solution of rectangular fuzzy game with pay-off as imprecise numbers instead of crisp numbers viz., interval and LR-type trapezoidal fuzzy numbers. The solution of such fuzzy games with pure strategies by minimax-maximin principle is discussed. The algebraic method to solve 2 × 2 fuzzy games without saddle point by using mixed strategies is also illustrated. Here m × n payoff matrix is reduced to 2 × 2 pay-off matrix by dominance method. This fact is illustrated by means of numerical example.

Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.