智能车载协作系统中车辆快速移动使得无线通信信道具有时变特性,为有效评估系统的误码性能,给出了符合车载时变信道的一阶自回归(AR1)模型,提出了一种基于AR1模型的自适应解码转发(ADF)协作误码率分析方法。该方法通过AR1模型的多普勒...智能车载协作系统中车辆快速移动使得无线通信信道具有时变特性,为有效评估系统的误码性能,给出了符合车载时变信道的一阶自回归(AR1)模型,提出了一种基于AR1模型的自适应解码转发(ADF)协作误码率分析方法。该方法通过AR1模型的多普勒频偏相关系数来刻画时变信道特性,根据中继译码结果自适应选择是否协作转发,提升了智能交通系统的可靠性。此外,利用矩生成函数(MGF)推导出ADF协作下多进制正交幅度调制(M-QAM)信号误码率封闭表达式,并分析了车载移动速度和信道状态信息(CSI)估计精度对误码性能的影响。数值仿真结果表明,车载系统能通过增加CSI估计精度,有效地减少车载快速移动引起的误码平顶值。该方法相对于放大转发(AF)协作通信方式,平均误码性能提高约8.7 d B。展开更多
[Objective] The research aimed to construct the discriminant classification model of DNA sequence by combining with the biology knowledge and the mathematical method.[Method] According to the polarity nature of side c...[Objective] The research aimed to construct the discriminant classification model of DNA sequence by combining with the biology knowledge and the mathematical method.[Method] According to the polarity nature of side chain radical in the amino acid,the classification information of amino acid which represented the sequence characteristic from the content and array situation of base was extracted from the different sequences that the amino acid content was different.The four-dimension vector was used to represent.Mahalanobis distance and Fisher discriminant methods were used to classify the given sequence.[Result] In the model,the back substitution rates of sample obtained by two kinds of classification methods were both 100%,and the consistent rate of classification was 90%.[Conclusion] In the model,the calculation method was simple,and the accuracy of classification result was higher.It was superior to the discriminant classification model which was only based on the base content.展开更多
The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecul...The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five categories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also considered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence fragments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.展开更多
文摘智能车载协作系统中车辆快速移动使得无线通信信道具有时变特性,为有效评估系统的误码性能,给出了符合车载时变信道的一阶自回归(AR1)模型,提出了一种基于AR1模型的自适应解码转发(ADF)协作误码率分析方法。该方法通过AR1模型的多普勒频偏相关系数来刻画时变信道特性,根据中继译码结果自适应选择是否协作转发,提升了智能交通系统的可靠性。此外,利用矩生成函数(MGF)推导出ADF协作下多进制正交幅度调制(M-QAM)信号误码率封闭表达式,并分析了车载移动速度和信道状态信息(CSI)估计精度对误码性能的影响。数值仿真结果表明,车载系统能通过增加CSI估计精度,有效地减少车载快速移动引起的误码平顶值。该方法相对于放大转发(AF)协作通信方式,平均误码性能提高约8.7 d B。
基金Supported by Science Research Project of Ningbo Dahongying University in2011(CF102601)~~
文摘[Objective] The research aimed to construct the discriminant classification model of DNA sequence by combining with the biology knowledge and the mathematical method.[Method] According to the polarity nature of side chain radical in the amino acid,the classification information of amino acid which represented the sequence characteristic from the content and array situation of base was extracted from the different sequences that the amino acid content was different.The four-dimension vector was used to represent.Mahalanobis distance and Fisher discriminant methods were used to classify the given sequence.[Result] In the model,the back substitution rates of sample obtained by two kinds of classification methods were both 100%,and the consistent rate of classification was 90%.[Conclusion] In the model,the calculation method was simple,and the accuracy of classification result was higher.It was superior to the discriminant classification model which was only based on the base content.
基金Supported by the Natural Science Foundation of Zhejiang Province(LY13A010007)~~
文摘The features of DNA sequence fragments were extracted from the distribution density of the condons in the individual cases of DNA sequence fragments. Based on the polarity of side chain radicals of amino acids molecules, the amino acids were classified into five categories, and the frequencies of these five categories were calculated. This kind of feature extraction based on the biological meanings not only took the content of basic groups into consideration, but also considered the marshal ing sequence of the basic groups. The hierarchical clustering analysis and BP neural network were used to classify the DNA sequence fragments. The results showed that the classification results of these two kinds of algo-rithms not only had high accuracy, but also had high consistence, indicating that this kind of feature extraction was superior over the traditional feature extraction which only took the features of basic groups into consideration.
