Momentos nulos e regularidade wavelet na detecção de falhas em sinais

Pós-graduação em Engenharia Elétrica - FEIS

Estimativa do expoente de Hurst de séries temporais de chuvas do estado de São Paulo usando as transformadas de Fourier, Wavelets e análise R/S

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimativa do expoente de Hurst, por meio da transformada Wavelet, de séries temporais de precipitação de chuvas das regiões climáticas do estado de São Paulo no período de 1978 a 1997

Pós-graduação em Física - IGCE

Análise de vibrações em sistemas discretos de massas concentradas e com dois graus de liberdade através da transformada wavelet

Pós-graduação em Física - IGCE

A transformada wavelet na fusão de imagens de sensoriamento remoto

Pós-graduação em Matematica Aplicada e Computacional - FCT

Um método para melhoria de qualidade de imagens médicas utilizando a transformada wavelet

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Um método não-limiar para redução de ruído em sinais de voz no domínio wavelet

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise e síntese de um processador digital wavelet

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Redução de ruído em sinais de voz no domínio wavelet

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Proteção Diferencial de Transformadores Usando a Transformada Wavelet

Pós-graduação em Engenharia Elétrica - FEIS

Processamento de fotomicrografias por meio da transformada wavelet starlet

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Behavioral and EEG effects of GABAergic manipulation of the nigro-tectal pathway in the Wistar audiogenic rat (WAR) strain II: An EEG wavelet analysis and retrograde neuronal tracer approach

The role of the substantia nigra pars reticulata (SNPr) and superior colliculus (SC) network in rat strains susceptible to audiogenic seizures still remain underexplored in epileptology. In a previous study from our laboratory, the GABAergic drugs bicuculline (BIC) and muscimol (MUS) were microinjected into the deep layers of either the anterior SC (aSC) or the posterior SC (pSC) in animals of the Wistar audiogenic rat (WAR) strain submitted to acoustic stimulation, in which simultaneous electroencephalographic (EEG) recording of the aSC, pSC, SNPr and striatum was performed. Only MUS microinjected into the pSC blocked audiogenic seizures. In the present study, we expanded upon these previous results using the retrograde tracer Fluorogold (FG) microinjected into the aSC and pSC in conjunction with quantitative EEG analysis (wavelet transform), in the search for mechanisms associated with the susceptibility of this inbred strain to acoustic stimulation. Our hypothesis was that the WAR strain would have different connectivity between specific subareas of the superior colliculus and the SNPr when compared with resistant Wistar animals and that these connections would lead to altered behavior of this network during audiogenic seizures. Wavelet analysis showed that the only treatment with an anticonvulsant effect was MUS microinjected into the pSC region, and this treatment induced a sustained oscillation in the theta band only in the SNPr and in the pSC. These data suggest that in WAR animals, there are at least two subcortical loops and that the one involved in audiogenic seizure susceptibility appears to be the pSC-SNPr circuit. We also found that WARs presented an increase in the number of FG + projections from the posterior SNPr to both the aSC and pSC (primarily to the pSC), with both acting as proconvulsant nuclei when compared with Wistar rats. We concluded that these two different subcortical loops within the basal ganglia are probably a consequence of the WAR genetic background. (C) 2012 Elsevier Inc. All rights reserved.

Multiresolution spherical wavelet analysis in global seismic tomography

Every seismic event produces seismic waves which travel throughout the Earth. Seismology is the science of interpreting measurements to derive information about the structure of the Earth. Seismic tomography is the most powerful tool for determination of 3D structure of deep Earth's interiors. Tomographic models obtained at the global and regional scales are an underlying tool for determination of geodynamical state of the Earth, showing evident correlation with other geophysical and geological characteristics. The global tomographic images of the Earth can be written as a linear combinations of basis functions from a specifically chosen set, defining the model parameterization. A number of different parameterizations are commonly seen in literature: seismic velocities in the Earth have been expressed, for example, as combinations of spherical harmonics or by means of the simpler characteristic functions of discrete cells. With this work we are interested to focus our attention on this aspect, evaluating a new type of parameterization, performed by means of wavelet functions. It is known from the classical Fourier theory that a signal can be expressed as the sum of a, possibly infinite, series of sines and cosines. This sum is often referred as a Fourier expansion. The big disadvantage of a Fourier expansion is that it has only frequency resolution and no time resolution. The Wavelet Analysis (or Wavelet Transform) is probably the most recent solution to overcome the shortcomings of Fourier analysis. The fundamental idea behind this innovative analysis is to study signal according to scale. Wavelets, in fact, are mathematical functions that cut up data into different frequency components, and then study each component with resolution matched to its scale, so they are especially useful in the analysis of non stationary process that contains multi-scale features, discontinuities and sharp strike. Wavelets are essentially used in two ways when they are applied in geophysical process or signals studies: 1) as a basis for representation or characterization of process; 2) as an integration kernel for analysis to extract information about the process. These two types of applications of wavelets in geophysical field, are object of study of this work. At the beginning we use the wavelets as basis to represent and resolve the Tomographic Inverse Problem. After a briefly introduction to seismic tomography theory, we assess the power of wavelet analysis in the representation of two different type of synthetic models; then we apply it to real data, obtaining surface wave phase velocity maps and evaluating its abilities by means of comparison with an other type of parametrization (i.e., block parametrization). For the second type of wavelet application we analyze the ability of Continuous Wavelet Transform in the spectral analysis, starting again with some synthetic tests to evaluate its sensibility and capability and then apply the same analysis to real data to obtain Local Correlation Maps between different model at same depth or between different profiles of the same model.

Wavetrack, implementazione ottimizzata di un algoritmo di pitch tracking basato su wavelet

WaveTrack é un'implementazione ottimizzata di un algoritmo di pitch tracking basato su wavelet, nello specifico viene usata la trasformata Fast Lifting Wavelet Transform con la wavelet di Haar. La libreria è stata scritta nel linguaggio C e tra le sue peculiarità può vantare tempi di latenza molto bassi, un'ottima accuratezza e una buona flessibilità d'uso grazie ad alcuni parametri configurabili.

Wavelet-denoising of electroencephalogram and the absolute slope method: a new tool to improve electroencephalographic localization and lateralization

OBJECTIVE: In ictal scalp electroencephalogram (EEG) the presence of artefacts and the wide ranging patterns of discharges are hurdles to good diagnostic accuracy. Quantitative EEG aids the lateralization and/or localization process of epileptiform activity. METHODS: Twelve patients achieving Engel Class I/IIa outcome following temporal lobe surgery (1 year) were selected with approximately 1-3 ictal EEGs analyzed/patient. The EEG signals were denoised with discrete wavelet transform (DWT), followed by computing the normalized absolute slopes and spatial interpolation of scalp topography associated to detection of local maxima. For localization, the region with the highest normalized absolute slopes at the time when epileptiform activities were registered (>2.5 times standard deviation) was designated as the region of onset. For lateralization, the cerebral hemisphere registering the first appearance of normalized absolute slopes >2.5 times the standard deviation was designated as the side of onset. As comparison, all the EEG episodes were reviewed by two neurologists blinded to clinical information to determine the localization and lateralization of seizure onset by visual analysis. RESULTS: 16/25 seizures (64%) were correctly localized by the visual method and 21/25 seizures (84%) by the quantitative EEG method. 12/25 seizures (48%) were correctly lateralized by the visual method and 23/25 seizures (92%) by the quantitative EEG method. The McNemar test showed p=0.15 for localization and p=0.0026 for lateralization when comparing the two methods. CONCLUSIONS: The quantitative EEG method yielded significantly more seizure episodes that were correctly lateralized and there was a trend towards more correctly localized seizures. SIGNIFICANCE: Coupling DWT with the absolute slope method helps clinicians achieve a better EEG diagnostic accuracy.