Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
Thetotal variation (TV) model has been studied extensively because it is able to preserve sharp attributes and capture some sparsely\ncritical information in images. However, TV denoising problem is usually ill-conditioned that the classical monotone projected\ngradient method cannot solve the problem efficiently. Therefore, a new strategy based on nonmonotone approach is digged out as\naccelerated spectral project gradient (ASPG) for solving TV. Furthermore, traditional TV is handled by vectorizing, which makes\nthe scheme farmore complicated for designing algorithms. In order to simplify the computing process, a newtechnique is developed\nin view of matrix rather than traditional vector. Numerical results proved that our ASPG algorithm is better than some state-ofthe-\nart algorithms in both accuracy and convergence speed....
This paper presents a multiple artificial neural networks (MANN) method with interaction\nnoise for estimating the occurrence probabilities of different classes at any site in space. The MANN\nconsists of several independent artificial neural networks, the number of which is determined by the\nneighbors around the target location. In the proposed algorithm, the conditional or pre-posterior\n(multi-point) probabilities are viewed as output nodes, which can be estimated by weighted\ncombinations of input nodes: two-point transition probabilities. The occurrence probability of\na certain class at a certain location can be easily computed by the product of output probabilities\nusing Bayes� theorem. Spatial interaction or redundancy information can be measured in the form\nof interaction noises. Prediction results show that the method of MANN with interaction noise has\na higher classification accuracy than the traditional Markov chain random fields (MCRF) model and\ncan successfully preserve small-scale features....
In this paper, a reweighted sparse representation algorithm based on noncircular sources\nis proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input\nmultiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special\nstructure of banded symmetric Toeplitz mutual coupling matrices (MCM), the proposed algorithm\nfirstly eliminates the effect of mutual coupling by linear transformation. Then, a reduced dimensional\ntransformation is exploited to reduce the computational complexity of the proposed algorithm.\nFurthermore, by utilizing the noncircular feature of signals, the new extended received data matrix is\nformulated to enlarge the array aperture. Finally, based on the new received data, a reweighted matrix\nis constructed, and the proposed method further designs the joint reweighted sparse representation\nscheme to achieve the DOA estimation by solving the l1-norm constraint minimization problem.\nThe proposed method enlarges the array aperture due to the application of signal noncircularity,\nand in the presence of mutual coupling, the proposed algorithm provides higher resolution and\nbetter angle estimation performance than ESPRIT-like, l1-SVD and l1-SRDML (sparse representation\ndeterministic maximum likelihood) algorithms. Numerical experiment results verify the effectiveness\nand advantages of the proposed method....
Robust channel estimation is required for coherent demodulation in multipath fading\nwireless communication systems which are often deteriorated by non-Gaussian noises. Our research\nis motivated by the fact that classical sparse least mean square error (LMS) algorithms are very\nsensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent\nsparsity information of wireless channels. This paper proposes a sign function based sparse adaptive\nfiltering algorithm for developing robust channel estimation techniques. Specifically, sign function\nbased least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described\nby a symmetric �±-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms\nare proposed by introducing several effective sparse-promoting functions into the standard SLMS\nalgorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they\noutperform the standard SLMS algorithm for robust sparse channel estimation, which can be also\nverified by simulation results....
Satellite remote sensing image target matching recognition exhibits poor robustness and accuracy because of the unfit feature\nextractor and large data quantity. To address this problem, we propose a new feature extraction algorithm for fast target matching\nrecognition that comprises an improved feature from accelerated segment test (FAST) feature detector and a binary fast retina key\npoint (FREAK) feature descriptor. To improve robustness, we extend the FAST feature detector by applying scale space theory and\nthen transformthe feature vector acquired by the FREAK descriptor fromdecimal into binary.We reduce the quantity of data in the\ncomputer and improve matching accuracy by using the binary space. Simulation test results show that our algorithm outperforms\nother relevant methods in terms of robustness and accuracy....
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