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848 JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 and Automation Engineering, Vo.01, pp. 358-360, February 2010. [7] Yun-Jie Xu, Wen-Bin Li, “Fault diagnosis for gearbox based on genetic-SVM classifier,” 2010 The 2nd International Conference on Computer and Automation Engineering, Vo.01, pp. 361-363, February 2010. [8] Yun-Jie Xu, Shu-Dong Xiu, “Prediction of wear for wood planning tool based on genetic-SVM classifier,” 2010 International Conference on Electrical and Control Engineering, Vol.01, pp.5834-5836, June 2010. [9] Yun-Jie Xu, Shu-Dong Xiu, “Accurate diagnosis of rolling bearing based on wavelet packet and genetic-support vector machine,” 2010 International Conference on Electrical and Control Engineering, Vol.01, pp.5589-5591, June 2010. [10] Guo Kui, Yu Dan, “Spreading L-M method of multiple reliability evaluation,” Reliability Engineering, Vol.04, pp.157-160, 2003. [11] Jie Yu, Yao-Lin Shi , Gui-Xiang Shen , Ya-zhou Jia, “Reliability evaluation on CNC lathes based on the modified L-M method,” Vol.16, pp.665-668, May 2009. [12] Jian-Pei Zhang, Zhong-Wei Li and Jing Yang, “A parallel SVM training algorithm on large-scale classification problems,” Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, vol. 01, pp. 1637-1641, August 2005. [13] KANGY W., LI J., CAO G. Y., “Dynanue temperature model in go fan so fusing Least square support vector machines,” Journal of Power sources, vol. 179, pp.683- 692, 2008. [14] Ling-Jun Li, Zhou-Suo Zhang, Zheng-Jia He, “Research of mechanical system fault diagnosis based on support vector data description,” Journal of Xi'an Jiaotong University, vol. 09, pp.910-913, 2003. [15] D. Wu, C.-W. Ma and S.-F. Du, “Influences of different damaged degrees of ieafminer-infected leaves on the nearinfrared spectral reflectance,” Trans. of the CSAE, vol. 23, no. 2, pp. 156-159, 2007. [16] Choy KL, Lee WB. Design of an intelligent supplier relationship management system: a hybrid case based neural network approach. Expert Systems with Application, vol.24, pp. 225-237, 2003. [17] Yang BS, Han T, Kim YS, “Integration of ART-Kohonen neural network and case-based reasoning for intelligent fault diagnosis,” Expert System with Applications, vol. 26, pp. 387-395, 2004. [18] Wen-Hong Li, Shao-Wen Sun, Qi Zhang, “Machinery fault diagnosis expert system based on case-based reasoning,” Journal of Chongqing University: English Edition, vol. 06, pp. 273-277, December. 2007. [19] Sajja, Akerkar, “Advanced knowledge based systems: model, applications & research,” TMRF e-Book, Vol.01, pp.50 -73, 2010. [20] Su Myat Marlar Soe and May Paing Paing Zaw, “Design and implementation of rule-based expert system for fault management,” World Academy of Science, Engineering and Technology 48, pp. 34-39, 2008. © 2011 ACADEMY PUBLISHER [21] Wei Liang, Mechanical fault diagnostics. Bei Jing: China coal industry publishing house. 2005. [22] Lefebvre, Mario, Applied Probability and Statistics [electronic book] by Mario Lefebvre. New York, NY: Springer Science +Business Media LLC.2005. [23] Wan-Lu Jiang, Shu-Qing Zhang, Yi-Qun Wang, Chaos and Wavelet Based Fault Information Diagnosis. Bei Jing: China Machine Prees.2005. [24] Jun Yang, Intelligent Fault Diagnosis Technology for Equipments. Bei Jing: National Defense Industry Press. 2004. Yun-Jie Xu was born in Neimenggu, China, in 1976. He received the B.S. degree in fluid power transmission and control from Dongbei University of Mechanical Engineering, Shenyang, China, in 1998, and the M.S. degree in Mechanical Design and Theory from Zhejiang University of Mechanical and Energy Engineering, Hangzhou, China, in 2004. He is currently pursuing the Ph.D. degree in forest engineering, University Of Beijing Forestry, Beijing, China, in 2009. From April 2004 to Dec. 2010, He is serves an lecturer of the School of Engineering, Zhejiang Agricultural & Forestry University. His research interests include system fault diagnosis and signal propagation in forest. Shu-Dong Xiu received B.S. and M.S. degrees in harbin institute of technology, Heilongjiang, China, in 1994, and 1988, respectively. From April 2004 to Dec. 2010, he was a faculty with the School of Engineering, Zhejiang Agricultural & Forestry University and was promoted to be an professor in 2009. His current research interests include forestry machinery and woodworking equipment. Quan-Sheng Men received B.S. degrees in Zhejiang University of Technology, Zhejiang, China, in 1989, respectively. From April 2001 to Dec. 2010, he was a faculty with the School of Engineering, Zhejiang Agricultural & Forestry University and was promoted to be an senior technician in 2010. His current research interests include forestry machinery and woodworking equipment. Liang Fang received M.S. degrees in Jiangsu University, Jiangsu, China, in 2007. From September 2007 to Dec. 2010, he was a faculty with the School of technology, Zhejiang Agricultural & Forestry University and was promoted to be an Laboratory Technician in 2007. His current research interests include mechatronic control, signal detection and processing.
JOURNAL OF SOFTWARE, VOL. 6, NO. 5, MAY 2011 849 A Novel Gray Image Watermarking Scheme Chen Yongqiang School of Computer Science, Wuhan Textile University, Wuhan 430073, China Email:chenyqwh@gmail.com Zhang Yanqing Department of Computer Science, Georgia State University, Atlanta 30303, USA Email: yzhang@zmail.cs.gsu.edu Hu Hanping Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, China Email: hphu@mail.hust.edu.cn Ling Hefei College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China Email: lhf@mail.hust.edu.cn Abstract—An effective and integrated image watermarking scheme mainly includes watermark generation, watermark embedding, watermark identification, and watermark attack. In this paper, a novel discrete wavelet transform domain image watermark scheme is proposed to meet the watermarking properties: security, imperceptibility and robustness. Here watermark comes from a meaningful binary image encrypted by two-dimensional chaotic stream encryption, which has more security. In the procedure of watermark embedding, the watermark is embedded into host image through selecting and modifying the wavelet coefficients using genetic algorithms with a simple fitness function to improve the imperceptibility of watermarked image. In order to identify the owner of extracted watermark, synergetic neural networks are used in the watermarking identification to overcome the limitation of correlation analysis or the human sense organ after some attacks. The results of our scheme realization and robust experiments show that this scheme has preferable performance. Index Terms—image watermark, genetic algorithm, synergetic neural networks, discrete wavelet transform I. INTRODUCTION Digital watermark is a kind of technology that embeds copyright information into multimedia data[1]. Unlike encryption, which is useful for transmission but does not provide a way to examine the original data in its protected form, the watermark remains in the content in its original form and does not prevent a user from Manuscript received November 15, 2010; revised January 21, 2011; accepted January 1, 2011. Corresponding author: Chen Yongqiang Project number: National 863 Hi-Tech Grant 2009AA01Z411 © 2011 ACADEMY PUBLISHER doi:10.4304/jsw.6.5.849-856 listening to, viewing, examining, or manipulating the content. Digital watermarking technology opens a new door to authors, producers, publishers, and service providers for protecting their rights and interests in multimedia documents. An effective image watermarking scheme mainly includes watermark generation, watermark embedding, watermark identification, and watermark attack. Watermark generation refers to what content and form of data a watermarking scheme adopts as watermark. The data may be original or encrypted from copyright information of number, letter, image, and so on. Some of copyright information is meaningful or meaningless. Meaningful information could be easily authenticated and usually needs to be encrypted in practice to strengthen watermarking security [2]. Watermark embedding is the most important part in a watermarking scheme and must meet the two most fundamental requirements under the condition of fixed watermark size, imperceptibility and robustness. The two requirements are in conflict with each other and need to reach a trade-off. Watermark embedding can be done in either spatial domain or frequency domain. The spatial domain watermark embedding manipulates host image pixels, especially on least significant bits that have less perceptual effect on the image[3]. Although the spatial domain watermark embedding is simple and easy to implement, it is less robust than frequency domain watermark embedding to various attacks and noise, which is made on the frequency coefficients of the host image. The existing frequency transformation methods for watermark embedding include discrete Fourier transform (DFT) [4], discrete cosine transform (DCT)[5], and discrete wavelet transform (DWT)[6]. Considering watermarking imperceptibility, we need to select an
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Aims and Scope. Call for Papers and
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