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1982 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 Engineering University and Space Control and Inertial Technology Research Center, Harbin Institute of Technology for technical support. REFERENCES [1] Liu feng, Sun dapeng, Huang yu, Tao ran, Wang yue,in :“Multi-component LFM signal feature extraction based on improved Wigner-Hough transform,” Journal of Beijing Technology University.(2008.10) [2] Ashok Narayanan V, Prabhu K M M. The fractional fourier transform:theory, implementation and error analysis[J]. Elsevier Micorprocessors and Microsystems, Vol.27(2003),p.511-521 [3] Ella Blngham and Aapo Hyvarinen. “A Fast Fixed-Point Algorithm For Independent Component Analysis Of Complex Valued Signals”. International Journal of Neural Systems, Vol.10, No.1(February,2000),p.1-8 [4] J.Herault and C.Jutten.Blind separation of sources, part: an adaptive algorithm based on neuro mimetic. Signal Processing, Vol.24(1)(1991),p.1-10. [5] LIU Q S , LU H Q , MA S D, “A Non-parameter Bayesian Classifier for Face Recognition [J] ,”Journal of Electronics (China), Vol.20(5)(2003),p.362 -370. [6] Shimizu S., Hyvarinen A., Kano Y.. A generalized least squares approach to blind separation of sources which have variance dependencies[J].Statistical Signal Processing, IEEE/SP 13th Workshop on(2005),p.1080-1083 [7] Tachibana K., Saruwatari H., Mori Y.. Efficient Blind Source Separation Combining Closed-Form Second-Order ICA and Nonclosed-Form Higher-Order ICA. IEEE International Conference on Acoustics,Speech and Signal Processing. ICASSP 2007. Vol. 1(2007), p.I-45-I-48 [8] Chee-Ming Ting, Salleh S.-H., Zainuddin Z.Z.. Spectral Estimation of Nonstationary EEG Using Particle Filtering With Application to Event-Related Desynchronization (ERD) [J]. IEEE Transactions on Biomedical Engineering. Vol. 58(2011) p.321-331 [9] Zou Hong-xing, LU Xu-guang, DAI Qiong-hai. Nonexistence of cross-term free time-frequency distribution with concentration of Wigner-ville distribution, Vol.3(2002) [10] Yuan junquan,Sun minqi,Sun xiaoxu, “LFM signal parameters estimation method based on Wigner Hough Transform”,Aerospace Electronic Countermeasures, Vol.6 (2004). [11] Solvang H.K.,Nagahara Y.,Araki S.. Frequency-Domain Pearson Distribution Approach for Independent Component Analysis (FD-Pearson-ICA) in Blind Source Separation[J]. IEEE Transactions on Audio,Speech,and Language Processing . Vol.17,No.4(2009),p.:639-648 [12] Liu ju, He zhenya, Zhang xianda. Blind Source Separation and Blind Deconvolution. Electronics Journal, Vol.30(4) (2002),p.570-576 [13] Li xiaoju,Zhu xiaolong,Zhang xianda. Blind source separation classification and prospects. Journal of Xi'an University of Electronic Science and Technology, Vol.31(3) (2004),p.399–404 [14] Zhang xianda, Bao zheng. Blind signal separation. E- Journal. Vol.29(12)( 2001),p.1766-1771. [15] Zou hong. Time-frequency analysis of multi-component LFM signals [D]. Xi'an Electronic Science and Technology University, 2000. [16] Liu Jiancheng, Wang Xuesong, Xiao Shunping, et a1. Radial acceleration estimation based on Wigner-Hough transform[J]. Acta Electronica Sinica, Vol.33(12) (2005),p.2236-2238. © 2011 ACADEMY PUBLISHER Qiang Guo was born in 1972. He received the B.S., M.S. and Ph.D. degree from Harbin Engineering University in information and communication engineering in 1994, 2003, and 2007, respectively. He is now an associate professor of information and communication engineering at Harbin Engineering University and a postdoctoral fellow of control science and engineering at Harbin Institute of Technology (HIT), China. His current research interests include radar signals sorting and recognition. More complex and dense pulses environments in modern electronic warfare present a severe challenge to the problem of radar signal sorting. Based on fractal theory and Hilbert-Huang Transform (HHT), he presented a new feature extraction method for radar pulse signal sorting. It used structure function and empirical mode decomposition to process 2-dimension feature information, which constituted carrier frequency and time-of-arrival. The same scheme also applied to the analysis and extraction of hidden periodically changing features—G features. Experiment results show that the method can effectively identify the agile frequency in periodically changing radio frequency signals of complex pulse environment, therefore provides a new feature for signal sorting of interleaved radar pulse sequences. He received the national 100 excellent doctor degree dissertation candidate nomination in 2009. He is now Academic Degree & Graduate Education Evaluating expert of MOE. Yajun Li was born in 1983. He respectively received the B.S., M.S. degree from YaTai University and Harbin Engineering University in information and communication engineering in 2008, 2011. He is now an Ph.D. at Harbin Institute of Technology (HIT), China. His current research interests include radar signals sorting and detection. At present, he has already published seven articles(EI index). Changhong Wang was born in 1961. He received the B.S. the M.S. and Ph.D. degree from Harbin Institute of Technology in 1983, 1986, and 1991, respectively. He is currently a professor and Ph.D. student supervisor of Harbin Institute of Technology. His research interests are mainly in inertial navigation, precise servo control system, and robust control.
JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 1983 Research on Self-built Digital Resource Backup Systems Abstract – This paper discussed the characteristics of the self-built digital resources and the requirement for long-term preservation. A backup system for self-built digital resources has been proposed based on the software and hardware features of the resources. Furthermore, simple analysis has been carried out on the proposed system. Index Terms – self-built digital resource, backup system, long-term preservation, data duplication elimination I. INTRODUCTION Digitalized preservation, organization and sharing are very important in digital resources construction. Self-built digital resources is an important part in digital resources construction. Self-built digital resources often appear in the form of feature databases self-created by libraries, for example, numerous well developed feature databases, dissertation databases and textbook databases are planned in library projects. Furthermore, different schools and universities also construct their own databases based on their discipline and regional characteristics, technical specialty, as well as financial budget [1]. In order to preserve self-built digital resources for long time, two aspects have to be considered, namely, how to prevent unauthorized modification and breach to digital information, and how to maintain long-term readability and authenticity of digital information. Technology is readily available to tackle the first problem, as a number of mature techniques have been proposed world widely to prevent illegal modification and breach of digital information; therefore, it is possible to solve the first problem to some extent if technical measures can be scientifically integrated with management practice. However, how to effectively maintain long-term readability of the digital information is still an open research area, no perfect solution has been proposed so far. The major difficulties lie on the deep involvement of numerous issues in which the most important one is the adoption of standards. Adopting standards can ease the conflicts between the technological update and readability of digital information. Nevertheless, problems still remain as some standards, particularly industrial standards are commonly outdated; and it is also difficult to completely comply with standards in practice. Currently, techniques used for long term preservation © 2011 ACADEMY PUBLISHER doi:10.4304/jcp.6.9.1983-1987 Li-zhen Shen Wenzhou University, Wenzhou, Zhejiang, China a wzu-slz@wzu.edu.cn include migration technique, updating technique, conversion technique, simulation technology, and digitizing technique using graphic tablets, etc. [3]. II. CHARACTERISTICS OF BACKUP SYSTEMS FOR SELF-BUILT DIGITAL RESOURCES Self-built digital resources are diverse, including such resource types as WEB resources, electronic publications, scientific data, multimedia resources and electronic dissertations, etc. Furthermore, all self-built digital resources use internet to provide resource services, therefore, in addition to fully back up the content of databases, it is often required to backup server systems and data publishing environment (both will be referred as servers in the sequel). The servers which need to be backed up may run multiple operating systems such as SUN Solaris, LINUX, Windows NT, and Windows 2000, and some may have Microsoft SQL Server 2000 database and ORACLE running on them. Considering all these aspects, backup systems should have the following functions: Backup across operating systems. Backup systems should support data backup and recovery across different operating systems such as Microsoft Windows, Unix, and IBM Aix, that is, a backup server can back up data from multiple operating platforms, thus reduce operational complexity and lower total cost of the backup work. Automatic Backup. Backup uses system resources. In practice, a running backup job may take 60% of the CPU resource of a mini-computer server with average configuration. Besides, backup jobs will also occupy network bandwidth as well as other resources. Therefore, backups should be performed when the load of the servers is minimal, should avoid casting extra load on the servers in the peak hours. Obviously, it is essential to use unattended, automated backup systems to avoid the human interactions during the backup time which, most likely, happens at late nights or in public holidays. Support multiple backup strategies. The famous Pareto principle holds in the backup area, namely, 20% of the data is updated more frequently with a back up probability of 80%. If every time a full backup is performed, it will inevitably waste resources and time in some cases, thus full backup is sometimes not viable. What we need the daily backup to do is to backup the delta of two consequential full backups. Therefore, we should adopt the so-called incremental backup strategy
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