Topic
Multiresolution analysis
About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.
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Papers
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TL;DR: This paper evaluated the performances of both the wavelet transform discrete approaches and the coefficient combination methods when they are applied to fuse multi-spectral and panchromatic images and showed that keeping the combination method the same, the “a trous” algorithm works better than the Mallat algorithm for the fusion purpose.
23 citations
TL;DR: A new algorithm is proposed to solve the approximation capacity of wavelet network in the local domain when the training data are not dense enough and has as many advantages as LAMA and LAST, does better in the learning of nonuniform data and has high approximation accuracy.
23 citations
TL;DR: This work presents a new image coding scheme based on multiwavelet filter banks, where several hierarchical trees are constructed in the transform domain, and an extension of set partitioning in hierarchical trees algorithm is proposed to quantize multi wavelet coefficients.
Abstract: This work presents a new image coding scheme based on multiwavelet filter banks. First, two dimensional (2-D) multiwavelet decomposition is performed on the original image. Then, several hierarchical trees are constructed in the transform domain, and an extension of set partitioning in hierarchical trees algorithm is proposed to quantize multiwavelet coefficients. Our simulation shows that this scheme is effective and promising.
23 citations
TL;DR: The denoising layers, when integrated into feedforward and recurrent neural networks, were validated on three time series prediction problems: the logistic map, a rubber hardness time series, and annual average sunspot numbers.
Abstract: To avoid the need to pre-process noisy data, two special denoising layers based on wavelet multiresolution analysis have been integrated into layered neural networks. A gradient-based learning algorithm has been developed that uses the same cost function to set both the neural network weights and the free parameters of the denoising layers. The denoising layers, when integrated into feedforward and recurrent neural networks, were validated on three time series prediction problems: the logistic map, a rubber hardness time series, and annual average sunspot numbers. Use of the denoising layers improved the prediction accuracy in both cases.
23 citations
01 Dec 2008
TL;DR: Concepts from signal processing and wavelet theory are used to create fast and sensitive fault detection in transmission line and the artificial neural network was used to classify the fault location in the transmission line.
Abstract: This paper presents the approach to the problem of fast fault detection in transmission line. The idea is to use concepts from signal processing and wavelet theory to create fast and sensitive fault detection. Then, the artificial neural network was used to classify the fault location in the transmission line. In this study, the output signal of the speed deviations of generator are taken as the input for wavelet analysis. The ldquooscillation signaturesrdquo are recorded using multi resolution analysis (MRA) wavelet transform. The MRA decomposes the signal where the components are analyzed for their energy content and characteristic and then used as a feature for different classes and locations of the fault. The same features are also fed to the probabilistic neural network (PNN) to give the location and classification of the fault.
22 citations