在认知无线电网络中,为了有效使用空闲频谱资源,提升频谱利用率,提出了一种基于图论模型的改进蚁群算法。该方法在信息素更新时引入了混沌初始化和混沌扰动,改变了传统蚁群算法信息素更新单一的规则,提升了算法的寻优能力,进而加快了收...在认知无线电网络中,为了有效使用空闲频谱资源,提升频谱利用率,提出了一种基于图论模型的改进蚁群算法。该方法在信息素更新时引入了混沌初始化和混沌扰动,改变了传统蚁群算法信息素更新单一的规则,提升了算法的寻优能力,进而加快了收敛速度,同时在传统模型基础上考虑了认知用户选择信道的优先级,重新设定了干扰约束条件,增加了认知用户对信道的需求大小问题,进而改善了认知用户的公平性。将改进的蚁群算法(Improved Ant Cology Algorithm,IACA)应用于认知无线电频谱分配问题,结果表明,基于IACA的认知无线网络频谱分配方法具有更快的收敛速度和更好的寻优性能,能有效提高系统的网络效益。展开更多
Speaker adaptive test normalization （ATnorm） is the most effective approach of the widely used score normalization in text-flldependent speaker verification, which selects speaker adaptive impostor cohorts with an e...Speaker adaptive test normalization （ATnorm） is the most effective approach of the widely used score normalization in text-flldependent speaker verification, which selects speaker adaptive impostor cohorts with an extra development corpus in order to enhance the recognition performance. In this paper, an improved implementation of ATnorm that can offer overall significant advantages over the original ATnorm is presented. This method adopts a novel cross similarity measurement in speaker adaptive cohort model selection without an extra development corpus. It can achieve a comparable performance with the original ATnorm and reduce the computation complexity moderately. With the full use of the saved extra development corpus, the overall system performance can be improved significantly. The results are presented on NIST 2006 Speaker Recognition Evaluation data corpora where it is shown that this method provides significant improvements in system performance, with relatively 14.4% gain on equal error rate （EER） and 14.6% gain on decision cost function （DCF） obtained as a whole.展开更多
文摘在认知无线电网络中,为了有效使用空闲频谱资源,提升频谱利用率,提出了一种基于图论模型的改进蚁群算法。该方法在信息素更新时引入了混沌初始化和混沌扰动,改变了传统蚁群算法信息素更新单一的规则,提升了算法的寻优能力,进而加快了收敛速度,同时在传统模型基础上考虑了认知用户选择信道的优先级,重新设定了干扰约束条件,增加了认知用户对信道的需求大小问题,进而改善了认知用户的公平性。将改进的蚁群算法(Improved Ant Cology Algorithm,IACA)应用于认知无线电频谱分配问题,结果表明,基于IACA的认知无线网络频谱分配方法具有更快的收敛速度和更好的寻优性能,能有效提高系统的网络效益。
基金supported by France Telecom Research and Development Center, Beijing
文摘Speaker adaptive test normalization （ATnorm） is the most effective approach of the widely used score normalization in text-flldependent speaker verification, which selects speaker adaptive impostor cohorts with an extra development corpus in order to enhance the recognition performance. In this paper, an improved implementation of ATnorm that can offer overall significant advantages over the original ATnorm is presented. This method adopts a novel cross similarity measurement in speaker adaptive cohort model selection without an extra development corpus. It can achieve a comparable performance with the original ATnorm and reduce the computation complexity moderately. With the full use of the saved extra development corpus, the overall system performance can be improved significantly. The results are presented on NIST 2006 Speaker Recognition Evaluation data corpora where it is shown that this method provides significant improvements in system performance, with relatively 14.4% gain on equal error rate （EER） and 14.6% gain on decision cost function （DCF） obtained as a whole.
