Several geometric sequences have very low linear complexities when considered as sequences over GF（p）, such as the binary sequences of period q^n - 1 constructed by Chan and Games [1-2] （q is a prime power p^m, p i...Several geometric sequences have very low linear complexities when considered as sequences over GF（p）, such as the binary sequences of period q^n - 1 constructed by Chan and Games [1-2] （q is a prime power p^m, p is an odd prime） with the maximal possible linear complexity q^n-1 when considered as sequences over GF（2）. This indicates that binary sequences with high GF（2） linear complexities LC2 and low GF（p）-linear complexities LCp are not secure for use in stream ciphers. In this article, several lower bounds on the GF（p）-linear complexities of binary sequences is proved and the results are applied to the GF（p）-linear complexities of Blum-Blum-Shub, self-shrinking, and de Bruijn sequences. A lower bound on the number of the binary sequences with LC2 〉 LCD is also presented.展开更多
多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图...多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图像与虹膜图像分别进行压缩降维处理,得到相应的初始特征矩阵.然后将人脸与虹膜的初始特征矩阵进行组合,获得组合特征矩阵.同时,利用2DFLD算法对组合特征矩阵进行融合,获得了人脸与虹膜的融合特征.最后运用最小距离分类器进行识别.基于ORL(Olivetti Research Laboratory)人脸数据库和CASIA(Chinese Academy ofSciences,Institute of Automation)虹膜数据库的实验结果表明,该模型实现了特征层融合,不仅克服了"小样本"效应,而且有效提高了身份识别的正确识别率,为多生物特征身份识别提供了一种有效模型.展开更多
基金supported by the National Natural Science Foundation of China (10871068)
文摘Several geometric sequences have very low linear complexities when considered as sequences over GF（p）, such as the binary sequences of period q^n - 1 constructed by Chan and Games [1-2] （q is a prime power p^m, p is an odd prime） with the maximal possible linear complexity q^n-1 when considered as sequences over GF（2）. This indicates that binary sequences with high GF（2） linear complexities LC2 and low GF（p）-linear complexities LCp are not secure for use in stream ciphers. In this article, several lower bounds on the GF（p）-linear complexities of binary sequences is proved and the results are applied to the GF（p）-linear complexities of Blum-Blum-Shub, self-shrinking, and de Bruijn sequences. A lower bound on the number of the binary sequences with LC2 〉 LCD is also presented.
文摘多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图像与虹膜图像分别进行压缩降维处理,得到相应的初始特征矩阵.然后将人脸与虹膜的初始特征矩阵进行组合,获得组合特征矩阵.同时,利用2DFLD算法对组合特征矩阵进行融合,获得了人脸与虹膜的融合特征.最后运用最小距离分类器进行识别.基于ORL(Olivetti Research Laboratory)人脸数据库和CASIA(Chinese Academy ofSciences,Institute of Automation)虹膜数据库的实验结果表明,该模型实现了特征层融合,不仅克服了"小样本"效应,而且有效提高了身份识别的正确识别率,为多生物特征身份识别提供了一种有效模型.
