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Real-time 3D sound localization is an important technology for various applications such as camera steering systems, robotics audition, and gunshot direction. 3D sound localization adds a new dimension, but also significantly increases the computational requirements. Real-time 3D sound localization continuously processes large volumes of data for each possible 3D direction and acoustic frequency range...
Motion deblurring techniques have played important roles in many image processing applications. In this paper, a new algorithm is proposed for motion deblurring from a single image. The proposed algorithm introduces an anisotropic Patiral Differential Equation (PDE) method for latent image prediction and employs an adaptive optimization model in the blind deconvolution iterative step. Furthermore,...
We examine the influence of short-term international capital flow on industrial output. Empirically, we find mutual Granger causality between the short-term international capital flow and industrial output. We also analyze the relation between the short-term international capital flow and the outputs of other subsectors. We find the positive effects on sectors are selective.
Word sense induction is usually viewed as a cluster problem in natural language processing. The context of the target word is represented as a vector and the cluster algorithms such as k-means, EM are applied. Different from the traditional methods, we proposed a new way based on “one sense per collocation” assumption which is proposed by Yarwosky (1993). Each sentence which...
Coreference is a common linguistic phenomenon in natural language understanding, it plays an important role in simplifying the expression and linking up the context. In this paper, the algorithm of support vector machines is applied to solve the problem of Chinese coreference, we consider fully the important characteristics which related to coreference and integrate them effectively to build model...
Unsupervised learning plays an important role in knowledge exploration and discovery. Two basic examples of unsupervised learning are clustering and dimensionality reduction. In this paper, we introduce an improved model for clustering based on a hierarchical analysis method. In our model, there are three main steps. In the first step, we use a structural clustering model to find qualitative patterns...
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