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The wavelet transform hierarchically decomposes images with prescribed bases, while multilinear models search for optimal bases to adapt visual data. In this paper, we integrate these two approaches to compactly represent 2D images and 3D volume data. Once a wavelet (packet) decomposition has been performed, the coefficients are subdivided into small blocks most of which have small energy and are...
Visual data comprise of multiscale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multidimensional data set is transformed into a hierarchy of signals to expose its multiscale structures. The signal at each level of the hierarchy is further...
This paper proposed a novel region based novel multisensor image fusion algorithm. A graph based segmentation method and biorthogonal multiwavelet transform (BMWT) is combined to segment the features of the source images to produce a region map. Then we use a scheme, which calculates the characteristics of each region of sources in wavelet domain by averaging and selection, to fuse the images. The...
In this paper, we propose a new intrusion detection technology which combines feature extraction with wavelet clustering method. Our intrusion detection model setup has two phases, where the first phase is to project the input data into high dimensional space by using the discriminant vectors extracted by Kernel Fisher Discriminant Analysis. By using KFDA, we can reduce the dimension of the input...
This paper presents a robust fault detection and diagnosis (FDD) scheme for abrupt and incipient faults in a class of nonlinear dynamic systems. A nonlinear observer which synthesizes second order sliding mode techniques and wavelet networks is proposed for online monitoring. The second order sliding mode is designed to eliminate the effect of system uncertainties on the state observation. Moreover,...
An integrated multi-method system to analyze the neuroelectric source parameters of electroencephalography (EEG) signal is presented. In order to handle the large-scale high dimension data efficiently and provide a real-time localizer in EEG inverse problem, an improved isometric mapping algorithm is used to find the low dimensional manifolds from high dimensional recorded EEG. Then, based on reduced...
An integrated multi-method system to analyze the neuroelectric source parameters of electroencephalography (EEG) signal is presented. In order to handle the large-scale high dimension data efficiently and provide a real-time localizer in EEG inverse problem, an improved isometric mapping algorithm is used to find the low dimensional manifolds from high dimensional recorded EEG. Then, based on reduced...
Electrical impedance tomography (EIT) is a noninvasive technique to estimate the conductivity distribution inside the object. In EIT, driving currents are injected through the object and voltages are measured at the electrodes on the surface. Algorithms to estimate the conductivity distribution from the measured voltages are called reconstruction algorithms. Image reconstruction is a nonlinear inverse...
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