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  • 1
    Publication Date: 2014-01-01
    Description: This paper proposes a new method for cross array to estimate two-dimensional direction of arrival (2-D DOA) in the presence of mutual coupling. In this method, the array elements which are affected by the same mutual coupling are chosen onx-axis andz-axis, respectively. Then a new matrix is constructed with the proper entries of cross covariance matrix of the chosen elements outputs onx-axis andz-axis. Propagation method (PM) and rotational invariance techniques for uniform linear array (ULA) are utilized in the constructed matrix to obtain two parameters correlated with elevations and azimuths. While calculating and pairing the two parameters, only once eigendecomposing and several division operations are required with the relationship among the matrix, its eigenvalues, and corresponding eigenvectors. Simulations are presented to validate the performance of the proposed method.
    Print ISSN: 1687-5869
    Electronic ISSN: 1687-5877
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Hindawi
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  • 2
    Publication Date: 2014-01-01
    Description: Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset.
    Print ISSN: 2356-6140
    Electronic ISSN: 1537-744X
    Topics: Natural Sciences in General
    Published by Hindawi
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