This paper focuses on the applications of the support vector machines in solving the problem of blind recovery in digital communication systems.We introduce the technique of support vector machines briefly,the develop...This paper focuses on the applications of the support vector machines in solving the problem of blind recovery in digital communication systems.We introduce the technique of support vector machines briefly,the development of blind equalization and analyze the problems which need to be resolved of the blind problems.Then the applicability of support vector machines in blind problem is highlighted and deduced.Finally,merit and shortage of blind equalization using support vector machines which is already exist to be discussed and the direction of further research is indicated.展开更多
利用无监督学习的一类支持向量机(One Class Support Vector Machine,OCSVM)和随机场景图像序列,构造滚动更新的像元分类模型,实现红外焦平面盲元的在线检测。根据正常像元和异常像元数量和灰度特征的差异,以随机图像序列作为输入数据,...利用无监督学习的一类支持向量机(One Class Support Vector Machine,OCSVM)和随机场景图像序列,构造滚动更新的像元分类模型,实现红外焦平面盲元的在线检测。根据正常像元和异常像元数量和灰度特征的差异,以随机图像序列作为输入数据,使用OCSVM建立单一类别的像元分类模型,灰度变化的像元归为一类,其他像元不属于此类。由于随机图像序列的滚动更新,OCSVM模型及支持向量也随之更新。统计支持向量的频次,高频次支持向量对应的像元聚为一类,即为异常像元。以320×256中波红外图像序列为例,说明了OCSVM模型进行盲元检测的过程,检测结果与黑体定标的结果一致。基于随机场景和OCSVM模型的盲元检测方法摆脱了定标黑体的约束,提高了盲元检测的灵活性。展开更多
基金supported in part by the National Natural Science Foundation of China(No.60772060 )the project of NJUPT(No.NY207056)
文摘This paper focuses on the applications of the support vector machines in solving the problem of blind recovery in digital communication systems.We introduce the technique of support vector machines briefly,the development of blind equalization and analyze the problems which need to be resolved of the blind problems.Then the applicability of support vector machines in blind problem is highlighted and deduced.Finally,merit and shortage of blind equalization using support vector machines which is already exist to be discussed and the direction of further research is indicated.
文摘利用无监督学习的一类支持向量机(One Class Support Vector Machine,OCSVM)和随机场景图像序列,构造滚动更新的像元分类模型,实现红外焦平面盲元的在线检测。根据正常像元和异常像元数量和灰度特征的差异,以随机图像序列作为输入数据,使用OCSVM建立单一类别的像元分类模型,灰度变化的像元归为一类,其他像元不属于此类。由于随机图像序列的滚动更新,OCSVM模型及支持向量也随之更新。统计支持向量的频次,高频次支持向量对应的像元聚为一类,即为异常像元。以320×256中波红外图像序列为例,说明了OCSVM模型进行盲元检测的过程,检测结果与黑体定标的结果一致。基于随机场景和OCSVM模型的盲元检测方法摆脱了定标黑体的约束,提高了盲元检测的灵活性。
基金Supported by National Natural Science Foundation of China(Grant No:51279106)the Special Research Fund for the Doctoral Program of Higher Education of China(Grant No:20110073110009)
