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A hierarchical cellular network carrying multiple service classes (voice conversational and data streaming services) is considered, where a dynamic channel assignment is implemented. In the dynamic channel assignment scheme, the bandwidth of data streaming services can be dynamically allocated according to the current traffic loading in the system, and the(More)
This paper reviewed the approaches of error handling in spoken dialogue systems in the literature at first. To take advantage of domain knowledge and the characteristics of the errors in Chinese speech recognition, a similarity between text strings from speech recognizer and the correct words was defined. A similarity-based algorithm of error handling in(More)
In the paper we compared three neural networks -Koskopsilas the bidirectional associative memory (BAM) and the discrete Hopfield network (DHN) with the counter propagation network (CPN) for processing of noisy data. We probe into their commonness and distinctness. The experimental results show that de-noise results of three neural networks for weak noise(More)
The paper presents a method for surface reconstruction from large unorganized and noisy point sets without any normal or orientation information. Firstly, the outliers will be selected and deleted, and acquire new point sets being less noisy with improved method of fuzzy c-means clustering. Secondly, we compute the normal of the new point sets through PCA(More)
One of the most important steps in digital mammography is an adequate segmentation of possible abnormalities. This obviously minimizes errors in further stages such as in classification. However, several factors affect the proper segmentation of mammograms. Mammograms contain low signal to noise ratio (low contrast) and a complicated structured(More)
—This paper presents a unified model, consisting of a linear dynamic system and a bounded static nonlinear operator. Most discrete-time chaotic systems, such as chaotic neural networks, Chua's circuits, and Hénon map etc, can be transformed into this unified model. Based on the H ∞ performance analysis of the estimation error system between the unified(More)
The paper proposes a part-level features fusion method for 3D object classification. 3D object is represented as a combination of its constituent parts, which are reconstructed by superquadric models. The presented classification method is decomposed into three main phrases: part features extraction of 3D objects, training set construction and(More)
In this paper, an integrated processing framework is proposed for 3D scene reconstruction and interpretation. Superquadric-based 3D scene reconstruction from both 2D images and 3D data are developed, in which superquadric-based hierarchical description is implemented as the universal parametric representation of real 3D scene objects. A volumetric part(More)