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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Changjun Zhou
Dalian university
Computer Science & IT

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

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A Novel DWT-Based Watermarking for Image with The SIFT Yuan Xu Yuan Xu; Qiang Zhang; Changjun Zhou
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i1.904

Abstract

A kind of scale invariant features transformation (SIFT for short) operators on DWT domain are proposed for watermarking algorithm. Firstly, the low frequency of the image is obtained by DWT. And then the SIFT transformation is used to calculate the key feature points for the low frequency sub-image. Based on the chosen space’s key points with moderate scale, a circular area as watermark embedding area is constructed. According to the research and final results, the novel digital watermark algorithm is proposed benefiting from the characteristics of SIFT’s key points and local time-frequency of DWT. The algorithm not only has good robustness to resist on such operations as compression, shearing, noise addition, median filtering and scaling, but also has good inhibition to possible watermark fake verification.
Inferring Gene Regulatory Network from Bayesian Network Model Based on Re-Sampling Qian Zhang; Xuedong Zheng; Qiang Zhang; Changjun Zhou
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i1.907

Abstract

Nowadays, gene chip technology has rapidly produced a wealth of information about gene expression activities. But the time-series expression data present a phenomenon that the number of genes is in thousands and the number of experimental data is only a few dozen. For such cases, it is difficult to learn network structure from such data. And the result is not ideal. So it needs to take measures to expand the capacity of the sample. In this paper, the Block bootstrap re-sampling method is utilized to enlarge the small expression data. At the same time, we apply “K2+T” algorithm to Yeast cell cycle gene expression data. Seeing from the experimental results and comparing with the semi-fixed structure EM learning algorithm, our proposed method is successful in constructing gene networks that capture much more known relationships as well as several unknown relationships which are likely to be novel.
Co-Authors Qian Zhang Qiang Zhang Xuedong Zheng Yuan Xu Yuan Xu