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)

Mathematical tests about the existence and applications of PPS-wavelets in function approximation problems

Neural networks and wavelet transform have been recently seen as attractive tools for developing eficient solutions for many real world problems in function approximation. Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. So, mathematical model is a very important tool to guarantee the development of the neural network area. In this article we will introduce one series of mathematical demonstrations that guarantee the wavelets properties for the PPS functions. As application, we will show the use of PPS-wavelets in pattern recognition problems of handwritten digit through function approximation techniques.

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

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

Application of wavelets to the evaluation of phantom images for mammography quality control

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multifractal regime transition in a modified minority game model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Harmonics filtering and detection of disturbances using wavelets

Traditional mathematical tools, like Fourier Analysis, have proven to be efficient when analyzing steady-state distortions; however, the growing utilization of electronically controlled loads and the generation of a new dynamics in industrial environments signals have suggested the need of a powerful tool to perform the analysis of non-stationary distortions, overcoming limitations of frequency techniques. Wavelet Theory provides a new approach to harmonic analysis, focusing the decomposition of a signal into non-sinusoidal components, which are translated and scaled in time, generating a time-frequency basis. The correct choice of the waveshape to be used in decomposition is very important and discussed in this work. A brief theoretical introduction on Wavelet Transform is presented and some cases (practical and simulated) are discussed. Distortions commonly found in industrial environments, such as the current waveform of a Switched-Mode Power Supply and the input phase voltage waveform of motor fed by inverter are analyzed using Wavelet Theory. Applications such as extracting the fundamental frequency of a non-sinusoidal current signal, or using the ability of compact representation to detect non-repetitive disturbances are presented.

Multipath reduction from GPS double differences using wavelets: How far can we go?

Wavelets are being extensively used in Geodetic applications. In this paper, the Multi-Resolution Analysis (MRA) using wavelets is applied to pseudorange and carrier phase GPS double differences (DDs) in order to reduce multipath effects. The wavelets were already applied to GPS carrier phase DDs, but some questions remain: How good can be the results, and are all multipath effects reduced? The answers to these questions are discussed in this paper. Thus, the wavelet transform is used to decompose the DD signals, splitting them in lower resolution components. After the decomposition process, the wavelet shrinkage is performed by thresholding to eliminate the components relative to multipath effects. Then, the DD observation can be reconstructed. This new DD signal is used to perform the baseline processing. The daily multipath repeatability was verified. With the application of the proposed approach, the results showed that the reliability of the ambiguity resolution and accuracy of the results improved when compared with the standard procedure. Furthermore, the method showed to be very efficient computationally, because, it is not noticed, at practical level, difference in the time span between the processing with and without application of the proposed method. However, only the high frequency multipath was eliminated.

GPS ambiguity resolution and validation under multipath effects: Improvements using wavelets

Integer carrier phase ambiguity resolution is the key to rapid and high-precision global navigation satellite system (GNSS) positioning and navigation. As important as the integer ambiguity estimation, it is the validation of the solution, because, even when one uses an optimal, or close to optimal, integer ambiguity estimator, unacceptable integer solution can still be obtained. This can happen, for example, when the data are degraded by multipath effects, which affect the real-valued float ambiguity solution, conducting to an incorrect integer (fixed) ambiguity solution. Thus, it is important to use a statistic test that has a correct theoretical and probabilistic base, which has became possible by using the Ratio Test Integer Aperture (RTIA) estimator. The properties and underlying concept of this statistic test are shortly described. An experiment was performed using data with and without multipath. Reflector objects were placed surrounding the receiver antenna aiming to cause multipath. A method based on multiresolution analysis by wavelet transform is used to reduce the multipath of the GPS double difference (DDs) observations. So, the objective of this paper is to compare the ambiguity resolution and validation using data from these two situations: data with multipath and with multipath reduced by wavelets. Additionally, the accuracy of the estimated coordinates is also assessed by comparing with the ground truth coordinates, which were estimated using data without multipath effects. The success and fail probabilities of the RTIA were, in general, coherent and showed the efficiency and the reliability of this statistic test. After multipath mitigation, ambiguity resolution becomes more reliable and the coordinates more precise. © Springer-Verlag Berlin Heidelberg 2007.

Oropharyngeal dysphagia identification using wavelets and optimum path forest

The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results. ©2008 IEEE.