This paper investigates the security performance of a cooperative multicast-unicast system,where the users present the feature of high mobility.Specifically,we develop the non-orthogonal multiple access(NOMA)based ort...This paper investigates the security performance of a cooperative multicast-unicast system,where the users present the feature of high mobility.Specifically,we develop the non-orthogonal multiple access(NOMA)based orthogonal time frequency space(OTFS)transmission scheme,namely NOMAOTFS,in order to combat Doppler effect as well as to improve the spectral efficiency.Further,we propose a power allocation method addressing the trade-off between the reliability of multicast streaming and the confidentiality of unicast streaming.Based on that,we utilize the relay selection strategy,to improve the security of unicast streaming.In the context of multicastunicast streaming,our simulation findings validate the effectiveness of the NOMA-OTFS based cooperative transmission,which can significantly outperform the existing NOMA-OFDM in terms of both reliability and security.展开更多
Particle filters have been widely used in nonlinear/non- Gaussian Bayesian state estimation problems. However, efficient distribution of the limited number of particles （n state space remains a critical issue in desi...Particle filters have been widely used in nonlinear/non- Gaussian Bayesian state estimation problems. However, efficient distribution of the limited number of particles （n state space remains a critical issue in designing a particle filter. A simplified unscented particle filter （SUPF） is presented, where particles are drawn partly from the transition prior density （TPD） and partly from the Gaussian approximate posterior density （GAPD） obtained by a unscented Kalman filter. The ratio of the number of particles drawn from TPD to the number of particles drawn from GAPD is adaptively determined by the maximum likelihood ratio （MLR）. The MLR is defined to measure how well the particles, drawn from the TPD, match the likelihood model. It is shown that the particle set generated by this sampling strategy is more close to the significant region in state space and tends to yield more accurate results. Simulation results demonstrate that the versatility and es- timation accuracy of SUPF exceed that of standard particle filter, extended Kalman particle filter and unscented particle filter.展开更多
Previous works have demonstrated that Laplacian embedding can well preserve the local intrinsic structure. However, it ignores the diversity and may impair the local topology of data. In this paper, we build an object...Previous works have demonstrated that Laplacian embedding can well preserve the local intrinsic structure. However, it ignores the diversity and may impair the local topology of data. In this paper, we build an objective function to learn the local intrinsic structure that characterizes both the local similarity and diversity of data, and then combine it with global structure to build a scatter difference criterion. Experimental results in face recognition show the effectiveness of our proposed approach.展开更多
Dimensionality reduction is very important in pattern recognition, machine learning, and image recognition. In this paper, we propose a novel linear dimensionality reduction technique using trace ratio criterion, name...Dimensionality reduction is very important in pattern recognition, machine learning, and image recognition. In this paper, we propose a novel linear dimensionality reduction technique using trace ratio criterion, namely Discriminant Neighbourhood Structure Embedding Using Trace Ratio Criterion (TR-DNSE). TR-DNSE preserves the local intrinsic geometric structure, characterizing properties of similarity and diversity within each class, and enforces the separability between different classes by maximizing the sum of the weighted distances between nearby points from different classes. Experiments on four image databases show the effectiveness of the proposed approach.展开更多
基金supported in part by the National Natural Science Foundation of China(No.61941105,No.61901327 and No.62101450)in part by the National Natural Science Foundation for Distinguished Young Scholar(No.61825104)+1 种基金in part by the Fundamental Research Funds for the Central Universities(JB210109)in part by the Foundation of State Key Laboratory of Integrated Services Networks of Xidian University(ISN22-03)。
文摘This paper investigates the security performance of a cooperative multicast-unicast system,where the users present the feature of high mobility.Specifically,we develop the non-orthogonal multiple access(NOMA)based orthogonal time frequency space(OTFS)transmission scheme,namely NOMAOTFS,in order to combat Doppler effect as well as to improve the spectral efficiency.Further,we propose a power allocation method addressing the trade-off between the reliability of multicast streaming and the confidentiality of unicast streaming.Based on that,we utilize the relay selection strategy,to improve the security of unicast streaming.In the context of multicastunicast streaming,our simulation findings validate the effectiveness of the NOMA-OTFS based cooperative transmission,which can significantly outperform the existing NOMA-OFDM in terms of both reliability and security.
基金supported by the National Natural Science Foundation of China(61271296)
文摘Particle filters have been widely used in nonlinear/non- Gaussian Bayesian state estimation problems. However, efficient distribution of the limited number of particles （n state space remains a critical issue in designing a particle filter. A simplified unscented particle filter （SUPF） is presented, where particles are drawn partly from the transition prior density （TPD） and partly from the Gaussian approximate posterior density （GAPD） obtained by a unscented Kalman filter. The ratio of the number of particles drawn from TPD to the number of particles drawn from GAPD is adaptively determined by the maximum likelihood ratio （MLR）. The MLR is defined to measure how well the particles, drawn from the TPD, match the likelihood model. It is shown that the particle set generated by this sampling strategy is more close to the significant region in state space and tends to yield more accurate results. Simulation results demonstrate that the versatility and es- timation accuracy of SUPF exceed that of standard particle filter, extended Kalman particle filter and unscented particle filter.
文摘Previous works have demonstrated that Laplacian embedding can well preserve the local intrinsic structure. However, it ignores the diversity and may impair the local topology of data. In this paper, we build an objective function to learn the local intrinsic structure that characterizes both the local similarity and diversity of data, and then combine it with global structure to build a scatter difference criterion. Experimental results in face recognition show the effectiveness of our proposed approach.
文摘Dimensionality reduction is very important in pattern recognition, machine learning, and image recognition. In this paper, we propose a novel linear dimensionality reduction technique using trace ratio criterion, namely Discriminant Neighbourhood Structure Embedding Using Trace Ratio Criterion (TR-DNSE). TR-DNSE preserves the local intrinsic geometric structure, characterizing properties of similarity and diversity within each class, and enforces the separability between different classes by maximizing the sum of the weighted distances between nearby points from different classes. Experiments on four image databases show the effectiveness of the proposed approach.
