We investigate the Andreev reflection across a uniaxial strained graphene-based superconducting junction.Compared with pristine graphene-based superconducting junction,three opposite properties are found.Firstly,in th...We investigate the Andreev reflection across a uniaxial strained graphene-based superconducting junction.Compared with pristine graphene-based superconducting junction,three opposite properties are found.Firstly,in the regime of the interband conversion of electron–hole,the Andreev retro-reflection happens.Secondly,in the regime of the intraband conversion of electron–hole,the specular Andreev reflection happens.Thirdly,the perfect Andreev reflection,electron–hole conversion with unit efficiency,happens at a nonzero incident angle of electron.These three exotic properties arise from the strain-induced anisotropic band structure of graphene,which breaks up the original relation between the direction of velocity of particle and the direction of the corresponding wavevector.Our finding gives an insight into the understanding of Andreev reflection and provides an alternative method to modulate the Andreev reflection.展开更多
Human limb movement imagery,which can be used in limb neural disorders rehabilitation and brain-controlled external devices,has become a significant control paradigm in the domain of brain-computer interface(BCI).Alth...Human limb movement imagery,which can be used in limb neural disorders rehabilitation and brain-controlled external devices,has become a significant control paradigm in the domain of brain-computer interface(BCI).Although numerous pioneering studies have been devoted to motor imagery classification based on electroencephalography(EEG)signal,their performance is somewhat limited due to insufficient analysis of key effective frequency bands of EEG signals.In this paper,we propose a model of multiband decomposition and spectral discriminative analysis for motor imagery classification,which is called variational sample-long short term memory(VS-LSTM)network.Specifically,we first use a channel fusion operator to reduce the signal channels of the raw EEG signal.Then,we use the variational mode decomposition(VMD)model to decompose the EEG signal into six band-limited intrinsic mode functions(BIMFs)for further signal noise reduction.In order to select discriminative frequency bands,we calculate the sample entropy(SampEn)value of each frequency band and select the maximum value.Finally,to predict the classification of motor imagery,a LSTM model is used to predict the class of frequency band with the largest SampEn value.An open-access public data is used to evaluated the effectiveness of the proposed model.In the data,15 subjects performed motor imagery tasks with elbow flexion/extension,forearm supination/pronation and hand open/close of right upper limb.The experiment results show that the average classification result of seven kinds of motor imagery was 76.2%,the average accuracy of motor imagery binary classification is 96.6%(imagery vs.rest),respectively,which outperforms the state-of-the-art deep learning-based models.This framework significantly improves the accuracy of motor imagery by selecting effective frequency bands.This research is very meaningful for BCIs,and it is inspiring for end-to-end learning research.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12104232,11805103,and 11804167)the Natural Science Foundation of Jiangsu Province,China(Grant Nos.BK20190137 and BK20180739)+1 种基金the Innovation Research Project of Jiangsu Province,China(Grant No.CZ0070619002)NJUPT-SF(Grant No.NY218128)。
文摘We investigate the Andreev reflection across a uniaxial strained graphene-based superconducting junction.Compared with pristine graphene-based superconducting junction,three opposite properties are found.Firstly,in the regime of the interband conversion of electron–hole,the Andreev retro-reflection happens.Secondly,in the regime of the intraband conversion of electron–hole,the specular Andreev reflection happens.Thirdly,the perfect Andreev reflection,electron–hole conversion with unit efficiency,happens at a nonzero incident angle of electron.These three exotic properties arise from the strain-induced anisotropic band structure of graphene,which breaks up the original relation between the direction of velocity of particle and the direction of the corresponding wavevector.Our finding gives an insight into the understanding of Andreev reflection and provides an alternative method to modulate the Andreev reflection.
基金This work was supported in part by the National Natural Science Foundation of China(Grant Nos.61876082,61861130366,61732006)National Key R&D Program of China(2018YFC2001600,2018YFC2001602).
文摘Human limb movement imagery,which can be used in limb neural disorders rehabilitation and brain-controlled external devices,has become a significant control paradigm in the domain of brain-computer interface(BCI).Although numerous pioneering studies have been devoted to motor imagery classification based on electroencephalography(EEG)signal,their performance is somewhat limited due to insufficient analysis of key effective frequency bands of EEG signals.In this paper,we propose a model of multiband decomposition and spectral discriminative analysis for motor imagery classification,which is called variational sample-long short term memory(VS-LSTM)network.Specifically,we first use a channel fusion operator to reduce the signal channels of the raw EEG signal.Then,we use the variational mode decomposition(VMD)model to decompose the EEG signal into six band-limited intrinsic mode functions(BIMFs)for further signal noise reduction.In order to select discriminative frequency bands,we calculate the sample entropy(SampEn)value of each frequency band and select the maximum value.Finally,to predict the classification of motor imagery,a LSTM model is used to predict the class of frequency band with the largest SampEn value.An open-access public data is used to evaluated the effectiveness of the proposed model.In the data,15 subjects performed motor imagery tasks with elbow flexion/extension,forearm supination/pronation and hand open/close of right upper limb.The experiment results show that the average classification result of seven kinds of motor imagery was 76.2%,the average accuracy of motor imagery binary classification is 96.6%(imagery vs.rest),respectively,which outperforms the state-of-the-art deep learning-based models.This framework significantly improves the accuracy of motor imagery by selecting effective frequency bands.This research is very meaningful for BCIs,and it is inspiring for end-to-end learning research.