目的探讨振幅整合脑电图(amplitude-integrated electroencephalogram,aEEG)、头颅磁共振成像(cranial magnetic resonance imaging,cMRI)的定量指标双顶径(biparietal width,BPW)、两半球间距(interhemispheric distance,IHD)与中晚期...目的探讨振幅整合脑电图(amplitude-integrated electroencephalogram,aEEG)、头颅磁共振成像(cranial magnetic resonance imaging,cMRI)的定量指标双顶径(biparietal width,BPW)、两半球间距(interhemispheric distance,IHD)与中晚期早产儿近期神经发育的关系。方法前瞻性选择2018年9月至2020年4月入住新生儿重症监护病房的104例中晚期早产儿为研究对象,在生后72 h内采用Naqeeb法及睡眠-觉醒周期(sleep-wake cycling,SWC)进行aEEG评估;在矫正胎龄37周时完成cMRI检查,并在T2冠状位测量BPW和IHD;矫正月龄6月龄时采用0~6岁儿童发育筛查测验(Developmental Screening Test for Child Under Six,DST)随访神经发育,并根据发育商(development quotient,DQ)分为DST正常组(≥85分,78例)和DST异常组(DQ<85分,26例),分析比较两组间各指标差异,以及aEEG和cMRI的关系。结果DST异常组aEEG正常率、SWC成熟率低于DST正常组(P<0.05);与DST正常组相比,DST异常组的IHD偏大、BPW偏小(P<0.05)。不成熟的SWC、aEEG异常、较大的IHD是DST异常的危险因素(P<0.05),较大的BPW是DST异常的保护因素(P<0.05)。结论中晚期早产儿生后72 h内的aEEG、矫正胎龄37周时cMRI定量指标BPW和IHD可能影响其矫正月龄6月龄时的神经发育结局。展开更多
In the exploration,tracking and positioning of underwater targets,it is necessary to perform frequency domain analysis and correlation calculation on the underwater acoustic signals of the target radiation.In a strong...In the exploration,tracking and positioning of underwater targets,it is necessary to perform frequency domain analysis and correlation calculation on the underwater acoustic signals of the target radiation.In a strong noise environment,the target signal may be overwhelmed by noise,resulting in an inability to effectively identify the target.Aiming at this problem,this paper presents a method of signal-noise separation by combining Fourier denoising with wavelet transform to realize underwater acoustic signal extraction in a strong noise environment.The combination algorithm of Fourier coefficient threshold adjustment and wavelet threshold transform is designed,and performance of the algorithm is tested.Simulation results show that the combination algorithm can effectively extract underwater acoustic signals when signal-to-noise ratio(SNR)is-15 dB,which can improve the SNR to 8.2 dB.展开更多
HB+协议是一种基于LPN(Learning Parity with Noise)问题的安全认证协议。由于LPN问题的非线性和复杂性,该协议的安全性能在量子计算机时代远远优于当前的非对称加密协议。基于以上特点,设计了一个以HB+协议为核心的新型安全认证系统,...HB+协议是一种基于LPN(Learning Parity with Noise)问题的安全认证协议。由于LPN问题的非线性和复杂性,该协议的安全性能在量子计算机时代远远优于当前的非对称加密协议。基于以上特点,设计了一个以HB+协议为核心的新型安全认证系统,采用密钥型物理非克隆函数引入随机噪声,并采用Toeplitz矩阵进行加密。仿真与实验结果表明,提出的基于HB+协议的认证系统在硬件实现上既简单又安全可靠。展开更多
文摘目的探讨振幅整合脑电图(amplitude-integrated electroencephalogram,aEEG)、头颅磁共振成像(cranial magnetic resonance imaging,cMRI)的定量指标双顶径(biparietal width,BPW)、两半球间距(interhemispheric distance,IHD)与中晚期早产儿近期神经发育的关系。方法前瞻性选择2018年9月至2020年4月入住新生儿重症监护病房的104例中晚期早产儿为研究对象,在生后72 h内采用Naqeeb法及睡眠-觉醒周期(sleep-wake cycling,SWC)进行aEEG评估;在矫正胎龄37周时完成cMRI检查,并在T2冠状位测量BPW和IHD;矫正月龄6月龄时采用0~6岁儿童发育筛查测验(Developmental Screening Test for Child Under Six,DST)随访神经发育,并根据发育商(development quotient,DQ)分为DST正常组(≥85分,78例)和DST异常组(DQ<85分,26例),分析比较两组间各指标差异,以及aEEG和cMRI的关系。结果DST异常组aEEG正常率、SWC成熟率低于DST正常组(P<0.05);与DST正常组相比,DST异常组的IHD偏大、BPW偏小(P<0.05)。不成熟的SWC、aEEG异常、较大的IHD是DST异常的危险因素(P<0.05),较大的BPW是DST异常的保护因素(P<0.05)。结论中晚期早产儿生后72 h内的aEEG、矫正胎龄37周时cMRI定量指标BPW和IHD可能影响其矫正月龄6月龄时的神经发育结局。
基金Applied Basic Research Project of Shanxi Province(Nos.201601D011035,201701D121067)Higher Education Technology Innovation Project of Shanxi Province(No.201804011)。
文摘In the exploration,tracking and positioning of underwater targets,it is necessary to perform frequency domain analysis and correlation calculation on the underwater acoustic signals of the target radiation.In a strong noise environment,the target signal may be overwhelmed by noise,resulting in an inability to effectively identify the target.Aiming at this problem,this paper presents a method of signal-noise separation by combining Fourier denoising with wavelet transform to realize underwater acoustic signal extraction in a strong noise environment.The combination algorithm of Fourier coefficient threshold adjustment and wavelet threshold transform is designed,and performance of the algorithm is tested.Simulation results show that the combination algorithm can effectively extract underwater acoustic signals when signal-to-noise ratio(SNR)is-15 dB,which can improve the SNR to 8.2 dB.
文摘HB+协议是一种基于LPN(Learning Parity with Noise)问题的安全认证协议。由于LPN问题的非线性和复杂性,该协议的安全性能在量子计算机时代远远优于当前的非对称加密协议。基于以上特点,设计了一个以HB+协议为核心的新型安全认证系统,采用密钥型物理非克隆函数引入随机噪声,并采用Toeplitz矩阵进行加密。仿真与实验结果表明,提出的基于HB+协议的认证系统在硬件实现上既简单又安全可靠。
