The transmission coefficients of electromagnetic （EM） waves due to a superconductor-dielectric superlattice are numerically calculated. Shift operator finite difference time domain （SO-FDTD） method is used in the ...The transmission coefficients of electromagnetic （EM） waves due to a superconductor-dielectric superlattice are numerically calculated. Shift operator finite difference time domain （SO-FDTD） method is used in the analysis. By using the SO-FDTD method, the transmission spectrum is obtained and its characteristics are investigated for different thicknesses of superconductor layers and dielectric layers, from which a stop band starting from zero frequency can be apparently observed. The relation between this low-frequency stop band and relative temperature, and also the London penetration depth at a superconductor temperature of zero degree are discussed, separately. The low-frequency stop band properties of superconductor-dielectric superlattice thus are well disclosed.展开更多
For the Hardy space H_E^2(R) over a ?at unitary vector bundle E on a ?nitely connected domain R, let TE be the bundle shift as [3]. If B is a reductive algebra containing every operator ψ(TE) for any rational functi...For the Hardy space H_E^2(R) over a ?at unitary vector bundle E on a ?nitely connected domain R, let TE be the bundle shift as [3]. If B is a reductive algebra containing every operator ψ(TE) for any rational function ψ with poles outside of R, then B is self adjoint.展开更多
Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof ...Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.展开更多
A closed form expression for the bit error rate (BER) performance of frequency domaindifferential demodulation(FDDD) for orthogonal frequency division multiplexing system in flat fadingchannel is derived.The performan...A closed form expression for the bit error rate (BER) performance of frequency domaindifferential demodulation(FDDD) for orthogonal frequency division multiplexing system in flat fadingchannel is derived.The performance is evaluated by computer simulation and compared with the timedomain differential demodulation(TDDD).The results indicate that the performance of FDDD is betterthan that of TDDD,and the lower band of BER in the former is lower than that of the latter.展开更多
基金Project supported partly by the Open Research Program in State Key Laboratory of Millimeter Waves of China(Grant No.K200802)partly by the National Natural Science Foundation of China(Grant No.60971122)
文摘The transmission coefficients of electromagnetic （EM） waves due to a superconductor-dielectric superlattice are numerically calculated. Shift operator finite difference time domain （SO-FDTD） method is used in the analysis. By using the SO-FDTD method, the transmission spectrum is obtained and its characteristics are investigated for different thicknesses of superconductor layers and dielectric layers, from which a stop band starting from zero frequency can be apparently observed. The relation between this low-frequency stop band and relative temperature, and also the London penetration depth at a superconductor temperature of zero degree are discussed, separately. The low-frequency stop band properties of superconductor-dielectric superlattice thus are well disclosed.
基金Project Supported by Scientific and Technological Research Program of Chongqing Municipal Education Commission(KJQN201801110)Chongqing Science and Technology Commission(CSTC2015jcyjA00045,cstc2018jcyjA2248)and NSFC(11871127)
文摘For the Hardy space H_E^2(R) over a ?at unitary vector bundle E on a ?nitely connected domain R, let TE be the bundle shift as [3]. If B is a reductive algebra containing every operator ψ(TE) for any rational function ψ with poles outside of R, then B is self adjoint.
文摘Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.
基金Supported by National Natural Science Foundation of China(No.60272009)and National 863 Plan Project(NO.2001AA1230131)
文摘A closed form expression for the bit error rate (BER) performance of frequency domaindifferential demodulation(FDDD) for orthogonal frequency division multiplexing system in flat fadingchannel is derived.The performance is evaluated by computer simulation and compared with the timedomain differential demodulation(TDDD).The results indicate that the performance of FDDD is betterthan that of TDDD,and the lower band of BER in the former is lower than that of the latter.
