Work place: Chang’ an University,Xi’ an, China
E-mail: shixin@chd.edu.cn
Website:
Research Interests: Image Compression, Image Manipulation, Computer Networks, Image Processing
Biography
Xin Shi was born in AnYang city, China, in January 1987. He received a B.E degree in Computer Science and Technology from the Chang’an University, China, in 2008. And he will receive the Master of engineering degree in Technology of Computer Application from the Chang’an University, China, in 2011. Currently, he is recommended to working towards a Ph.D degree at Traffic Information Engineering and Control, System Development Lab, Chang’an university. His areas of interest include Wireless Sensor Networks, Digital Image Processing.
DOI: https://doi.org/10.5815/ijeme.2011.05.03, Pub. Date: 29 Nov. 2011
For the requirements of knowledge reuse in product design process, according to the characteristics of the knowledge representation methods, this paper uses ontology knowledge representation method to construct the product design process knowledge model and gives ontology mapping decision strategy which is based on classification. In the basis of choosing "design department" ontology in human resource management as the heterogeneous ontology of "design organization" ontology in product design process management, lists their concepts set and calculates the similarity of matching concept pairs, finally, outputs the mapping relationship table.
[...] Read more.By Lan Yang Xiang-mo Zhao Fei Hui Xin Shi
DOI: https://doi.org/10.5815/ijitcs.2010.02.03, Pub. Date: 8 Dec. 2010
The de-noising of sensor data has become an important to research. Since the traditional de-noising method can’t achieve successful de-noising effect and the software-only method never meets a high real time capability. In this paper, we illustrate a novel threshold function based on the wavelet hard and soft threshold function. It is unlike ordinary function, which has overcome the defect such as the discontinuity of hard threshold function and an invariable dispersion between the estimated wavelet coefficients and the decomposed coefficients of soft threshold function. Moreover, we consider the hardware implementation of wavelet threshold filter on FPGA which adopt the pleated sheet structure of multiplier and fit to frame data. A detailed description of the simulation and implementation is given. Finally, the experiment result on-board is shown that our hardware implementation can meet the requirement of real-time signal processing.
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