便携为王 东芝M801笔记本

13.3英寸的东芝M801笔记本采用高亮超显炫彩屏,使用了TOSHIBA CSV超显亮技术,让画面更锐丽清晰,视觉效果更流畅。这里重点说的是,酷酷的银河面板漆不光外观风格时尚,还能有效屏蔽电磁辐射,实在不错。M801笔记本采用多种加密方式,如BIOS加密、文件加密、硬盘加密等,硬盘加密后,即使被盗,硬盘数据也无法被盗取,可放心使用。东芝M801笔记本对数据的安全保护也有独到之处。

商务黑吃黑 富士通S6410深度体验

还是在几年之前,商务笔记本电脑还像哲人柏拉图一样,让人觉得有高高在上的感觉,而随着这两年笔记本电脑技术的发展和平台的更替,以联想ThinkPad Xserles系列为代表的商务笔记本电脑逐渐放下了高贵的姿态,成为普通人也能购买得起的高端数码类产品。在我们看来,商务笔记本电脑的演进过程基本上代表了目前笔记本电脑发展历程。就目前的流行发展趋势来看,以英特尔迅驰平台为代表,笔记本电脑在短短4年时间里,经历了近4代的更替,而一直引领笔记本电脑技术和设计流行趋势的商务笔记本电脑产品,在此演变中扮演了极其重要的角色。

Fingerprint singular points extraction based on orientation tensor field and Laurent series

Singular point(SP)extraction is a key component in automatic fingerprint identification system(AFIS).A new method was proposed for fingerprint singular points extraction,based on orientation tensor field and Laurent series.First,fingerprint orientation flow field was obtained,using the gradient of fingerprint image.With these gradients,fingerprint orientation tensor field was calculated.Then,candidate SPs were detected by the cross-correlation energy in multi-scale Gaussian space.The energy was calculated between fingerprint orientation tensor field and Laurent polynomial model.As a global descriptor,the Laurent polynomial coefficients were allowed for rotational invariance.Furthermore,a support vector machine(SVM)classifier was trained to remove spurious SPs,using cross-correlation coefficient as a feature vector.Finally,experiments were performed on Singular Point Detection Competition 2010(SPD2010)database.Compared to the winner algorithm of SPD2010 which has best accuracy of 31.90%,the accuracy of proposed algorithm is 45.34%.The results show that the proposed method outperforms the state-of-the-art detection algorithms by large margin,and the detection is invariant to rotational transformations.

Auto-Aligned Sharing Fuzzy Fingerprint Vault

Recently, a cryptographic construct,called fuzzy vault, has been proposed for crypto-biometric systems, and some implementations for fingerprint have been reported to protect the stored fingerprint template by hiding the fingerprint features. However, all previous studies assumed that fingerprint features were pre-aligned, and automatic alignment in the fuzzy vault domain is a challenging issue.In this paper, an auto-aligned sharing fuzzy fingerprint vault based on a geometric hashing technique is proposed to address automatic alignment in the multiple-control fuzzy vault with a compartmented structure. The vulnerability analysis and experimental results indicate that, compared with original multiplecontrol fuzzy vault, the auto-aligned sharing fuzzy fingerprint vault can improve the security of the system.

A Comparative Analysis on Testing Stability of Seed Coat Neps (SCN) Number and Size with AFIS and Premier aQura

This paper makes a comparative analysis of testing stability of seed coat neps (SCN) number and size with Advanced Fiber Information System (AFIS) and aQura.After testing the number and size of SCN in sliver produced by two different experiments (twelve plans in each experiment)with AFIS and aQura,the test results are analyzed with the theory of statistical analysis and the following conclusions are drawn:(1) the testing stability of SCN number and size of aQura is better than that of AFIS;(2) to get a reliable testing stability of SCN number,more than 24 samples should be tested on aQura,while more than 130 samples on AFIS;(3) for SCN size test,more than 10 and 12 samples should be tested on aQura and AFIS,respectively;(4) the basic reason for higher testing stability of SCN number and size on aQura is that the weight of the samples is greater than that on AFIS.

Fingerprint Identification by Artificial Neural Network

Biometric techniques require critical operations of digital processing for identification of individuals. In this context, this paper aims to develop a system for automatic processing of fingerprint identification by their minutiae using Artificial Neural Networks （ANN）, which reveals to be highly effective. The ANN method implemented is a based on Multi-Layer Perceptron （MLP） model, which utilizes the algorithm of retro-propagation of gradient during the learning process. In such a process, the mean square error generated represents the specific parameter for the identification phase by comparing a fingerprint taken from a crime scene with those of a reference database.

Pegasus：a distributed and load-balancing fingerprint identification system

Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability. With the rapid development of fingerprint identification techniques, many fingerprint identification systems are in urgent need to deal with large-scale fingerprint storage and high concurrent recognition queries, which bring huge challenges to the system. In this circumstance, we design and implement a distributed and load-balancing fingerprint identification system named Pegasus, which includes a distributed feature extraction subsystem and a distributed feature storage subsystem. The feature extraction procedure combines the Hadoop Image Processing Interface（HIPI） library to enhance its overall processing speed; the feature storage subsystem optimizes MongoD B＇s default load balance strategy to improve the efficiency and robustness of Pegasus.Experiments and simulations are carried out, and results show that Pegasus can reduce the time cost by 70% during the feature extraction procedure. Pegasus also balances the difference of access load among front-end mongos nodes to less than 5%. Additionally, Pegasus reduces over 40% of data migration among back-end data shards to obtain a more reasonable data distribution based on the operation load（insertion, deletion, update, and query） of each shard.