目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singula...目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。展开更多
A quantum memory or information processing device is subject to the disturbance from its surrounding environment or the inevitable leakage due to its contact with other systems. To tackle these problems, several contr...A quantum memory or information processing device is subject to the disturbance from its surrounding environment or the inevitable leakage due to its contact with other systems. To tackle these problems, several control protocols have been proposed for quantum memory or storage. Among them, the fast signal control or dynamical decoupling based on external pulse sequences provides a prevailing strategy aimed at suppressing decoherence and preventing the target systems from the leakage or diffusion process. In this paper, we review the applications of this protocol in protecting quantum memory under the non-Markovian dissipative noise and maintaining systems on finite speed adiabatic passages without leakage therefrom. We analyze perturbative and nonperturbative dynamical equations for leakage and control, including second-order master equation, quantum-state-diffusion equation, and one-component master equation derived from Feshbach PQ-partitioning technique. It turns out that the quality of fast-modulated signal control is insensitive to configurations of the applied pulse sequences. Specifically, decoherence and leakage will be greatly suppressed as long as the control sequence is able to effectively shift the system beyond the bath cutoff frequency, almost independent of the details of the control sequences that could be ideal pulses, regular rectangular pulses, random pulses and even noisy pulses.展开更多
文章提出了一种有效分离并重构信号的数字化方案。信号A和信号B通过加法器叠加成信号C,对信号C进行快速傅里叶变换,再通过直接数字合成(Direct Digital Synthesizer,DDS)模块分别输出重构信号A′和重构信号B′。测试结果表明:当输入信号...文章提出了一种有效分离并重构信号的数字化方案。信号A和信号B通过加法器叠加成信号C,对信号C进行快速傅里叶变换,再通过直接数字合成(Direct Digital Synthesizer,DDS)模块分别输出重构信号A′和重构信号B′。测试结果表明:当输入信号为1时,V PP的正弦波或者三角波频率在10~100 kHz。所提方案能够有效分离出2路重构信号,重构信号无失真、无漂移,能够调节2路信号相位差。展开更多
基于深度学习进行信号自动调制识别在分类精度、可迁移性等方面普遍优于传统方法,引起广泛关注。但是,当前方法多数针对单信号样本进行识别,无法适用于混叠信号识别场景。针对该问题,对混叠信号调制识别方法进行了研究,结合长短期记忆(l...基于深度学习进行信号自动调制识别在分类精度、可迁移性等方面普遍优于传统方法,引起广泛关注。但是,当前方法多数针对单信号样本进行识别,无法适用于混叠信号识别场景。针对该问题,对混叠信号调制识别方法进行了研究,结合长短期记忆(long short term memory,LSTM)网络和深度残差收缩网络(deep residual shrinkage network,DRSN),设计了时序深度残差收缩网络模型,其中包含残差模块、收缩模块和LSTM模块。残差模块和收缩模块负责提取混叠信号中的显著信息并自适应生成决策阈值,LSTM模块用于提取混叠信号中的时序隐含特征。三者结合可以有效提高混叠信号的识别精度。公开和实测数据集测试结果表明,所提方法识别精度优于5种典型方法,在高信噪比下的平均识别分类准确率可以达到92.7%;21种混叠信号中有12种识别准确率接近100%。展开更多
A programmable high-accuracy system was proposed to collect and process telemetric fast varying signals,which consists of pre-circuit,analog-to-digital（A/D）conversion unit and signal collection and processing part.P...A programmable high-accuracy system was proposed to collect and process telemetric fast varying signals,which consists of pre-circuit,analog-to-digital（A/D）conversion unit and signal collection and processing part.Performance analysis demonstrates that this novel telemetry-acquisition method is a potential solution to rapidly process fast varying signals and efficiently utilize telemetry channel.展开更多
文摘目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。
基金supported by the Basque Government (IT472-10)the Spanish MICINN (FIS2012-36673-C03-03)+2 种基金the Basque Country University UFI (11/55-01-2013)the National Natural Science Foundation of China (11175110)the Science and Technology Development Program of Jilin Province of China (20150519021JH)
文摘A quantum memory or information processing device is subject to the disturbance from its surrounding environment or the inevitable leakage due to its contact with other systems. To tackle these problems, several control protocols have been proposed for quantum memory or storage. Among them, the fast signal control or dynamical decoupling based on external pulse sequences provides a prevailing strategy aimed at suppressing decoherence and preventing the target systems from the leakage or diffusion process. In this paper, we review the applications of this protocol in protecting quantum memory under the non-Markovian dissipative noise and maintaining systems on finite speed adiabatic passages without leakage therefrom. We analyze perturbative and nonperturbative dynamical equations for leakage and control, including second-order master equation, quantum-state-diffusion equation, and one-component master equation derived from Feshbach PQ-partitioning technique. It turns out that the quality of fast-modulated signal control is insensitive to configurations of the applied pulse sequences. Specifically, decoherence and leakage will be greatly suppressed as long as the control sequence is able to effectively shift the system beyond the bath cutoff frequency, almost independent of the details of the control sequences that could be ideal pulses, regular rectangular pulses, random pulses and even noisy pulses.
文摘文章提出了一种有效分离并重构信号的数字化方案。信号A和信号B通过加法器叠加成信号C,对信号C进行快速傅里叶变换,再通过直接数字合成(Direct Digital Synthesizer,DDS)模块分别输出重构信号A′和重构信号B′。测试结果表明:当输入信号为1时,V PP的正弦波或者三角波频率在10~100 kHz。所提方案能够有效分离出2路重构信号,重构信号无失真、无漂移,能够调节2路信号相位差。
文摘基于深度学习进行信号自动调制识别在分类精度、可迁移性等方面普遍优于传统方法,引起广泛关注。但是,当前方法多数针对单信号样本进行识别,无法适用于混叠信号识别场景。针对该问题,对混叠信号调制识别方法进行了研究,结合长短期记忆(long short term memory,LSTM)网络和深度残差收缩网络(deep residual shrinkage network,DRSN),设计了时序深度残差收缩网络模型,其中包含残差模块、收缩模块和LSTM模块。残差模块和收缩模块负责提取混叠信号中的显著信息并自适应生成决策阈值,LSTM模块用于提取混叠信号中的时序隐含特征。三者结合可以有效提高混叠信号的识别精度。公开和实测数据集测试结果表明,所提方法识别精度优于5种典型方法,在高信噪比下的平均识别分类准确率可以达到92.7%;21种混叠信号中有12种识别准确率接近100%。
文摘A programmable high-accuracy system was proposed to collect and process telemetric fast varying signals,which consists of pre-circuit,analog-to-digital（A/D）conversion unit and signal collection and processing part.Performance analysis demonstrates that this novel telemetry-acquisition method is a potential solution to rapidly process fast varying signals and efficiently utilize telemetry channel.
