Temat: wavelets - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Wavelets in the prediction of short-time series
Autorzy:: Hadaś-Dyduch, Monika
Tematy:: wavelets
prediction
Daubechies wavelets
Haar wavelet; Pokaż więcej
Wydawca:: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Powiązania:: https://bibliotekanauki.pl/articles/584990.pdf Link otwiera się w nowym oknie
Opis:: The aim of this article is to present original application wavelets to the prediction of short-term of time series. The model proposed to predict short-term time series (in particular for predicting macroeconomic indicators) is a model of copyright. The model is based on wavelet analysis, the Haar wavelet, the Daubechies wavelet and adaptive models. The Daubechies wavelets are a family of orthogonal wavelets and are characterized by a maximal number of vanishing moments for some given support. Adaptive models have been appropriately modified by the introduction of a wavelet function and combined into one predictive model. The results obtained from the study results indicate that the authorial model is an effective tool for short-term predictions. The model was applied to predict macroeconomic indicators.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Mathematical Economics 11(8) : Praca zbiorowa
Tematy:: Publikacje darmowe; Pokaż więcej
Dostawca treści:: IBUK Libra

Książka

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Skocz do pozycji: 3.

Tytuł:: Search for resolution invariant wavelet features of melanoma learned by a limited ANN classifier
Autorzy:: Surówka, Grzegorz
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

Artykuł

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Skocz do pozycji: 4.

Tytuł:: On optimal wavelet bases for classification of melanoma images through ensemble learning
Autorzy:: Ogorzałek, Maciej
Surówka, Grzegorz
Wydawca:: Springer
Opis:: This article addresses the medical problem of early detection of the malignant melanoma skin cancer. We present ensemble classification of dermoscopic skin lesion images into two classes: malignant melanoma and dysplastic nevus. The features used for classification are derived from wavelet decomposition coefficients of the image. Our research purpose is to select the best wavelet bases in terms of AUC classification performance of the ensemble. The ensemble learning is optimized by some common quality measures: accuracy, precision, F1-score, FP- rate, speci-ficity, BER and recall. Within the statistics of our machine learning experiments the best model of melanoma uses reverse bi-orthogonal wavelet pair (3.1) and is optimized by FP-rate. This wavelet base performs very well with downscaled image resolutions which matters future small ARMbased devices for computer aided diagnosis of melanoma.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

Inne

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Skocz do pozycji: 5.

Tytuł:: Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods
Autorzy:: Schluter, S.
Deuschle, C.
Tematy:: forecasting
wavelets
denoising
multiscale analysis; Pokaż więcej
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Powiązania:: https://bibliotekanauki.pl/articles/108362.pdf Link otwiera się w nowym oknie
Opis:: By means of wavelet transform, an ARIMA time series can be split into different frequency components. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own characteristies. Eventually we come to the conclusion that using wavelets does improve forecasting quality especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.
Dostawca treści:: Biblioteka Nauki

Artykuł

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Skocz do pozycji: 6.

Tytuł:: Ondelettes et poids de Muckenhoupt
Autorzy:: Gilles Lemarié-Rieusset, Pierre
Tematy:: singular integrals
wavelets
weighted Lebesgue spaces; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Matematyczny PAN
Powiązania:: https://bibliotekanauki.pl/articles/1291171.pdf Link otwiera się w nowym oknie
Opis:: We study, for a basis of Hölderian compactly supported wavelets, the boundedness and convergence of the associated projectors $P_j$ on the space $L^p(dμ)$ for some p in ]1,∞[ and some nonnegative Borel measure μ on ℝ. We show that the convergence properties are related to the $A_p$ criterion of Muckenhoupt.
Dostawca treści:: Biblioteka Nauki

Artykuł

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Skocz do pozycji: 7.

Tytuł:: Wavelet-neural systems as approximators of an unknown function - a comparison of biomedical signal classifiers
Autorzy:: Kostka, P.
Tkacz, E.
Tematy:: wavelets
neural networks
biomedical signal classifiers; Pokaż więcej
Wydawca:: Politechnika Gdańska
Powiązania:: https://bibliotekanauki.pl/articles/1965818.pdf Link otwiera się w nowym oknie
Opis:: Wavelet-neural systems (WNS) presented in this work, inheriting the properties of neural networks, belong to the class of universal approximators of unknown functions, F, describing the relationship between input X ∈ RN and output Y ∈ RM of a process or object. Classifier structures described in this work fulfil the role of approximators of functions, which are able to assign the input signal to a particular class with a given accuracy. A performance comparison of elaborated classifier structures with preliminary time-frequency analysis in the wavelet layer has been made for different types of the neural part. A feed forward multi-layer perceptron and a neural net with radial basic functions are analysed theoretically and practically. Results included in this paper present a comparison of the learning and verification stages of a classifier, tested on the basis of non-stationary signals of heart rate variability. Despite the fact that a WNS with the Morlet basic function gives the best results for the learning phase of WNS, the other tested wavelets used in the preliminary layer, Db4, allow us to obtain the best system performance during its verification.
Dostawca treści:: Biblioteka Nauki

Artykuł

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Skocz do pozycji: 8.

Tytuł:: Differentiation of random structure properties using wavelet analysis of backscattered ultrasound
Autorzy:: Gambin, B.
Wojcik, J.
Doubrovina, O.
Tematy:: spectrogram
scalogram
wavelets
random scattering structure; Pokaż więcej
Wydawca:: Polskie Towarzystwo Akustyczne
Powiązania:: https://bibliotekanauki.pl/articles/331612.pdf Link otwiera się w nowym oknie
Opis:: The aim of this work was to find the differences between random media by analyzing the properties of the ultrasound signals backscattered from the inhomogeneities. A numerical model is used to generate two types of random media. The first has the randomness in scatterers’ positions and the second has the randomness in the size and acoustical properties of scatterers. The numerical model of wave scattering has been used to simulate the RF (radio frequency) signals caused by the incident pulse traveling as a plane wave. The markers of randomness type differences between the scattering media were obtained with the help of the spectral and wavelet analysis. The effect of differences in randomness type is more spectacular when the wavelet analysis is performed.
Dostawca treści:: Biblioteka Nauki

Artykuł

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Skocz do pozycji: 9.

Tytuł:: Detecting sharp contours of images
Wykrywanie ostrych konturów obrazów
Autorzy:: Korzeniewski, Jerzy
Tematy:: wavelets theory
function cusps
sharp contours; Pokaż więcej
Wydawca:: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Powiązania:: https://bibliotekanauki.pl/articles/905374.pdf Link otwiera się w nowym oknie
Opis:: The theory of wavelets introduced by Daubechies is a developing branch of mathematics with a wide range of potential applications. This paper presents a survey of some methods of detecting sharp cusps of unknown function developed by Wang with examples of their application to detecting sharp contours of images. A simple original algorithm to detect sharp contours of two dimensional images is also proposed and its application is presented. Visual examination allows to state that the results are comparable with the Wang’s method.
Teoria falek wprowadzonych przez Daubechies jest rozwijającą się częścią matematyki, stosowaną w wielu dziedzinach. W artykule tym przedstawiony jest przegląd metod wykrywania „szpiców” nieznanej funkcji opracowanych przez Wanga. Metody wykrywania szpiców i skoków w przypadku jednowymiarowym są zilustrowane przykładem zastosowania do funkcji rzeczywistej jednej zmiennej, która ma jeden szpic oraz jeden skok (por. rys. 1). W przypadku dwuwymiarowym metody są zilustrowane przykładem zastosowania do wykrywania ostrych konturów obrazu przedstawiającego fotografię kobiety (por. rys. 2). Część 4 artykułu jest wkładem własnym autora. Zaproponowany jest algorytm rozpoznawania ostrych konturów obrazów, którego zaletą jest prostota oraz szybkość działania. Algorytm jest zastosowany do ustalenia ostrych konturów fotografii przedstawiającej martwą naturę. Wzrokowa ocena efektów algorytmu pozwala na stwierdzenie, że wyniki są porównywalne z wynikami uzyskanymi przez Wanga za pomocą metod opartych na skomplikowanym aparacie matematycznym oraz wolno działających.
Dostawca treści:: Biblioteka Nauki

Artykuł

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Skocz do pozycji: 10.

Tytuł:: Wavelet-based logistic discriminator of dermoscopy images
Autorzy:: Ogorzałek, Maciej
Surówka, Grzegorz
Opis:: Proper diagnosis of cutaneous melanoma is a life-saving factor. The most important limitation is the early and sensitive recognition of melanoma relative to dysplastic nevi. We have studied wavelet-based features extracted from dermoscopic images as efficient signals of neoplastic changes. We recursively treat the dermoscopic images and all their transformation channels (wavelet packets) through the Mallat transform. All the four decomposition filters from each decomposition level are the source of features based on three functions of the pixel values. We train the logistic classifier regularized by either the Lasso or the Ridge penalty and test its AUC metric for a set of different wavelet bases, and as a function of image resolution. A total of three different data sets with respectively 185, 117, and 413 images, and 52 wavelet bases are tested. Classification performance as a function of the wavelet number strongly depends on the image resolution and image compression. There is also a large variation in the classification performance within the self same wavelet family. Degradation of image resolution makes the overall classification performance lower and more dispersed between the regularizes. Some wavelets do not lower, but increase the learning performance at the reduced image resolutions, which is consistent with the melanoma feature-extraction studies based on other learning paradigms. The logistic classifier can extract high-performance, resolution-invariant wavelet features of melanoma.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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