29 resultados para Discrete Wavelet Transforms
em Bulgarian Digital Mathematics Library at IMI-BAS
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Mathematics Subject Classification: 42A38, 42C40, 33D15, 33D60
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ACM Computing Classification System (1998): I.7, I.7.5.
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Image content interpretation is much dependent on segmentations efficiency. Requirements for the image recognition applications lead to a nessesity to create models of new type, which will provide some adaptation between law-level image processing, when images are segmented into disjoint regions and features are extracted from each region, and high-level analysis, using obtained set of all features for making decisions. Such analysis requires some a priori information, measurable region properties, heuristics, and plausibility of computational inference. Sometimes to produce reliable true conclusion simultaneous processing of several partitions is desired. In this paper a set of operations with obtained image segmentation and a nested partitions metric are introduced.
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In this paper we examine discrete functions that depend on their variables in a particular way, namely the H-functions. The results obtained in this work make the “construction” of these functions possible. H-functions are generalized, as well as their matrix representation by Latin hypercubes.
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We study a class of models used with success in the modelling of climatological sequences. These models are based on the notion of renewal. At first, we examine the probabilistic aspects of these models to afterwards study the estimation of their parameters and their asymptotical properties, in particular the consistence and the normality. We will discuss for applications, two particular classes of alternating renewal processes at discrete time. The first class is defined by laws of sojourn time that are translated negative binomial laws and the second class, suggested by Green is deduced from alternating renewal process in continuous time with sojourn time laws which are exponential laws with parameters α^0 and α^1 respectively.
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* This research was supported by a grant from the Greek Ministry of Industry and Technology.
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In this paper a new double-wavelet neuron architecture obtained by modification of standard wavelet neuron, and its learning algorithm are proposed. The offered architecture allows to improve the approximation properties of wavelet neuron. Double-wavelet neuron and its learning algorithm are examined for forecasting non-stationary chaotic time series.
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* The work is supported by RFBR, grant 04-01-00858-a.
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The article describes the method of preliminary segmentation of a speech signal with wavelet transformation use, consisting of two stages. At the first stage there is an allocation of sibilants and pauses, at the second – the further segmentation of the rest signal parts.
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In this paper an outliers resistant learning algorithm for the radial-basis-fuzzy-wavelet-neural network based on R. Welsh criterion is proposed. Suggested learning algorithm under consideration allows the signals processing in presence of significant noise level and outliers. The robust learning algorithm efficiency is investigated and confirmed by the number of experiments including medical applications.
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A generalized convolution with a weight function for the Fourier cosine and sine transforms is introduced. Its properties and applications to solving a system of integral equations are considered.
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Mathematics Subject Classification: 44A05, 46F12, 28A78
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Mathematics Subject Classification: 26A33, 45K05, 60J60, 60G50, 65N06, 80-99.
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Mathematics Subject Classification: 43A20, 26A33 (main), 44A10, 44A15
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Mathematics Subject Classification: 33D15, 44A10, 44A20