目的分析感音神经性听力损失患者耳蜗死区(cochlear dead regions,CDR)检测结果。方法应用均衡噪声阈值TEN(HL)法检测273例302耳感音神经性听力损失患者的耳蜗死区,并分析耳蜗死区与其可能病因及听力曲线类型的关系。结果 273例(302耳)...目的分析感音神经性听力损失患者耳蜗死区(cochlear dead regions,CDR)检测结果。方法应用均衡噪声阈值TEN(HL)法检测273例302耳感音神经性听力损失患者的耳蜗死区,并分析耳蜗死区与其可能病因及听力曲线类型的关系。结果 273例(302耳)感音神经性听力损失患者中,112例117耳存在耳蜗死区,占总耳数的38.74%;存在耳蜗死区的患者在耳蜗死区频率处DPOAE均未引出,其中高频听力下降型占40.22%(74/184),低频下降型占47.14%(33/70),两端下降型占33.33%(2/6),平坦型占19.04%(8/42);噪声、耳毒性药物、突发性聋三种因素致聋者中耳蜗死区检出率较高。结论本组感音神经性听力损失患者耳蜗死区检出率约为38.74%,高频下降型者最多,噪声和/或耳毒性药物致聋者更易出现耳蜗死区。展开更多
Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex ...Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex lighting,and complex structure of face anti-spoofing detection network,a novel method is presented using a combination of convolutional neural network and brightness equalization.Firstly,multi-task convolutional neural network(MTCNN)based on the cascade of three convolutional neural networks(CNNs),P-net,R-net,and O-net are used to achieve accurate positioning of the face,and the detected face bounding box is cropped by a specified multiple,then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image.Finally,data features are extracted and classification is given by utilizing a 12-layer convolution neural network.Experiments of the proposed algorithm were carried out on CASIA-FASD.The results show that the classification accuracy is relatively high,and the half total error rate(HTER)reaches 1.02%.展开更多
文摘目的分析感音神经性听力损失患者耳蜗死区(cochlear dead regions,CDR)检测结果。方法应用均衡噪声阈值TEN(HL)法检测273例302耳感音神经性听力损失患者的耳蜗死区,并分析耳蜗死区与其可能病因及听力曲线类型的关系。结果 273例(302耳)感音神经性听力损失患者中,112例117耳存在耳蜗死区,占总耳数的38.74%;存在耳蜗死区的患者在耳蜗死区频率处DPOAE均未引出,其中高频听力下降型占40.22%(74/184),低频下降型占47.14%(33/70),两端下降型占33.33%(2/6),平坦型占19.04%(8/42);噪声、耳毒性药物、突发性聋三种因素致聋者中耳蜗死区检出率较高。结论本组感音神经性听力损失患者耳蜗死区检出率约为38.74%,高频下降型者最多,噪声和/或耳毒性药物致聋者更易出现耳蜗死区。
基金Project(61671204)supported by National Natural Science Foundation of ChinaProject(2016WK2001)supported by Hunan Provincial Key R&D Plan,China。
文摘Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex lighting,and complex structure of face anti-spoofing detection network,a novel method is presented using a combination of convolutional neural network and brightness equalization.Firstly,multi-task convolutional neural network(MTCNN)based on the cascade of three convolutional neural networks(CNNs),P-net,R-net,and O-net are used to achieve accurate positioning of the face,and the detected face bounding box is cropped by a specified multiple,then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image.Finally,data features are extracted and classification is given by utilizing a 12-layer convolution neural network.Experiments of the proposed algorithm were carried out on CASIA-FASD.The results show that the classification accuracy is relatively high,and the half total error rate(HTER)reaches 1.02%.
