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The power line channel is characterized by a multipath behavior. A direct consequence is that multi-carrier modulation (MCM) is generally retained for power line communication (PLC). In addition to OFDM, or equivalently DMT, wavelet OFDM and Hermitian symmetry OFDM/OQAM (HS-OQAM) have been recently proposed for PLC transmission. In this paper we propose a unified and efficient modulation scheme that...
The study of the behavior of ion-channels can provide significant information to detect metal ions and small organic molecules in solution. Discrimination of different analytes can be performed by extracting appropriate features from the ion-channel signals and using them for classification. In this paper, we consider features extracted from the Fourier, Wavelet and Walsh-Hadamard domain representations...
We present a new image restoration method based on modelling the coefficients of an overcomplete wavelet response to natural images with a mixture of two Gaussian distributions, having non-zero and zero mean respectively, and reflecting the assumption that this response is close to be sparse. Including the observation model, the resulting procedure iterates between image reconstruction from the hard-thresholding...
In this article we address the issue of denoising photon-limited image data by deriving new and efficient multivariate Bayesian estimators that approximate the conditional expectation of Haar wavelet and filterbank transform coefficients of Poisson data - coefficients that take the so-called Skellam distribution. We show that in this setting, the posterior mean under a Bayesian model forms the solution...
In this paper, a new image coding scheme based on a wavelet-like transform derived from orthogonal polynomial basis is presented. From a set of bivariate orthogonal polynomial functions, we first obtain the 2D non-separable wavelet functions to propose a wavelet-like transform coding. The motivation behind using orthogonal polynomials is that they exhibit some properties related to the human visual...
This paper presents a novel local image descriptor for dense wide-baseline matching purposes, coined SULD (speeded-up local descriptor). SULD approximates or even outperforms than previously proposed schemes such as SURF and DAISY, and can be computed and compared much faster. This is achieved by summing up the Haar wavelet responses rather than the gradient, by computing convolutions recursively...
This paper proposes to combine standard SVM classification with a hierarchical approach to increase SVM classification accuracy as well as reduce computational load of SVM testing. Support vectors are obtained by applying SVM training to the entire original training data. For classification, multi-level two-dimensional wavelet decomposition is applied to each hyperspectral image band and low spatial...
This paper presents a Wavelet Kernel Fisher Classifier (WKFC) for ferroresonance detection. Using this method Ferroresonance can be discriminate from other transients such as capacitor switching, load switching and transformer switching. Wavelet transform is used for decomposition of signals, feature selection is done by Kernel Principal Component Analysis (KPCA). The Fisher classifier is applied...
SIFT (scale invariant feature transform) is an important local invariant feature descriptor. Since its expensive computation, SURF (speeded-up robust features) is proposed. Both of them are designed mainly for gray images. However, color provides valuable information in object description and matching tasks. To overcome the drawback and to increase the descriptor's distinctiveness, this paper presents...
In order to detect abnormal events of chemical processes, a new fault detection method based on kernel principal component analysis (KPCA) is described. Firstly, it removes the noise from data set using wavelet packet transform (WPT). Secondly, a feature vector selector schemes (FVS) based on a geometrical consideration is given to reduce the computation complexity of KPCA when the number of the samples...
To accurately predict the non-stationary time series, an approach based on integration of wavelet transform, PSO (Particle Swarm Optimization) and SVM (Support Vector Machine) is proposed. Wavelet decomposition is used to reduce the complexity of time series. Different components are predicted by their corresponding SVM forecasters, respectively, after wavelet transform. The final forecasting result...
Wavelet modulation (WM) signal is a special kind of multi-carrier modulation signals (MCMS). Based on the time-frequency characteristics of WM signal, this paper use the mixed moments of the adaptive optimal kernel (AOK) time-frequency distribution to study the identification of multi-carrier modulation signals. Simulation results show that, this method can separate wavelet modulation signal from...
Accurate traffic flow forecasting is the key to the development of intelligent transportation systems (ITS). However, the classical forecasting method using the support vector regression (SVR) based on RBF kernel does not support online learning and has the problems of information loss, long processing time, low robustness and so on. An effective Marr Wavelet kernel which we combine the wavelet theory...
Traffic flow is a fundamental measure in transportation. Accurate traffic flow prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. A novel multiscale wavelet support vector regression method (MW-SVR) is proposed for traffic flow prediction. Based on wavelet multi-resolution analysis, a scaling kernel function with multi-resolution...
In this paper, we present a methodology to classify grasp types based on two channel forearm electromyogram signals. Six grasp types are identified. Classification is through support vector machine using radial basis function kernel based on sum of wavelet decomposition coefficients of the electromyogram signals. In a study involving six subjects, we achieved an average recognition rate of 86%; better...
Soft defined radio is again the research issue because of cognitive radio, Modulation type recognition (MTR) is the key issue of the soft defined radio, in this paper, a new MTR method based on the wavelet support vector machine (WSVM) has been proposed, we derive the WSVM kernel function and utilize it to classify the modulation type, the results show when the SNR threshold for the modulation scheme...
We propose a kernel function called wavelet assignment graph kernel for graph classification which has applications in drug discovery. This is an extension of wavelet alignment graph kernel. In this method we use graphs to model chemical compounds. For feature extraction we have applied wavelet analysis to graph structured chemical structure, for each atom we collect features about the atom and its...
Continuous wavelet transforms of multivariable vector function spaces are discussed. In the weak topology we get the reconstruction formulas of the continuous wavelet transforms of multivariable vector function spaces produced by the integral kernel of the transform multivariable vector functions and those of it produced by the integral kernel of the multivariable vector functions which are different...
Support vector machine (SVM), which is based on statistical learning theory, is a universal machine learning method. This paper proposes the application of SVM in classifying the causes of voltage sag in power distribution system. Voltage sag is among the major power quality disturbances that can cause substantial loss of product and also can attribute to malfunctions, instabilities and shorter lifetime...
In this study, detection of small target in chaotic clutter with unknown dynamics is presented. We achieve this in four steps: (i) by using db3 wavelet decomposition of the signals, (ii) using Takens delay embedding theorem and least-squares support vector machine (LS-SVM) prediction, including increase the symmetric constraint and improve the kernel function, (iii) wavelet reconstruction, (iv) separation...
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