17076624
Performance Bound Analysis and Retiming of Timed Circuits
Timed asynchronous circuits are efficient in performance and power consumption. Traditional performance analysis method can not analyze timed circuits efficiently. In this paper, we model timed circuits using timed Petri net and digraph. We studied the mean cycle time of timed Petri net model. The upper bound and lower bound of mean cycle time were given. Then we proposed an algorithm for timed circuits retiming. The algorithm can efficiently distribute buffers along communication channels of timed circuits to gain maximal performance and minimal area. These algorithms were applied to phased logic circuitspsila design and optimization.
45238482
TengYue-1TengYue: In Chinese means jump over.: A High Performance Embedded SoC
21334257
Feature Extraction Based on the Independent Component Analysis for Text Classification
The independent component analysis (ICA) is a very popular algorithm used in the blind source separation and it has been widely used in many other fields. In this paper, the ICA is applied to text classification. We try to combine the traditional feature selection methods with ICA technology to improve the text classification performance by extracting Independent features. Further, a series of comparison experiments have been performed. The experiment results have shown that the ICA technology can indeed help to improve the classification performance and the combined method has showed the clear advantages.
62767012
Incorporating prior knowledge into SVM for image retrieval
SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.
368510
A Performance Model for Domino Mail Server
Using performance model to analyze the Internet service is prevailing. However, the research of the performance model for mail server retrains in the immature status. In this paper, based on the queue theory, we propose a performance model for Domino mail server. The verification and analysis of the model are also presented. The experimental results show that this model characterizes the behavior of Domino mail server well and prove that it is useful for the qualitative performance measurement of Domino mail server.
29695672
Managing and Mining Multimedia Data, Animations and Annotations
Several advances have been made on managing multimedia data stores. We now have products for multimedia data management. This article provides an overview of managing multimedia databases and describes techniques for managing multimedia animations and annotations as well as mining multimedia data. 
 
 
Keywords: 
 
data mining; 
mulitimedia databases; 
animations; 
images; 
multimedia database; 
management system
14748707
Training One-class Support Vector Machines in the Primal Space
In this paper, we proposed a novel Newton-type solver for one-class support vector machines in the primal space directly. Firstly, utilizing reproducing property of kernel and Huber regression function, original constrained quadratic programming is transformed into approximate unconstrained one, which is continuous and twice differentiable. Then, we give a Newton-type training algorithm to solve it. Further analysis shows the computation complexity of our algorithm is identical with theoretical lower bound for solving one-class support vector machines. In the end, experiments on 9 UCI datasets are done to validate the affectivity of proposed algorithm, and when comparing with dual method (LIBSVM),its produces comparative testing accuracy, better training speed, and less support vectors.