This paper describes a brain-inspired simultaneous localization and mapping(SLAM)system using oriented features from accelerated segment test and rotated binary robust independent elementary(ORB)features of RGB(red,gr...This paper describes a brain-inspired simultaneous localization and mapping(SLAM)system using oriented features from accelerated segment test and rotated binary robust independent elementary(ORB)features of RGB(red,green,blue)sensor for a mobile robot.The core SLAM system,dubbed RatSLAM,can construct a cognitive map using information of raw odometry and visual scenes in the path traveled.Different from existing RatSLAM system which only uses a simple vector to represent features of visual image,in this paper,we employ an efficient and very fast descriptor method,called ORB,to extract features from RCB images.Experiments show that these features are suitable to recognize the sequences of familiar visual scenes.Thus,while loop closure errors are detected,the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm.Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.展开更多
This paper provides a method to study the solution of equations for syn- chronous binary stars with large eccentricity on the main sequence. The theoretical results show that the evolution of the eccentricity is linea...This paper provides a method to study the solution of equations for syn- chronous binary stars with large eccentricity on the main sequence. The theoretical results show that the evolution of the eccentricity is linear with time or follows an exponential form, and the semi-major axis and spin vary with time in an exponen- tial form that are different from the results given in a previous paper. The improved method is applicable in both cases of large eccentricity and small eccentricity. In ad- dition, the number of terms in the expansion of a series with small eccentricity is very long due to the series converging slowly. The advantage of this method is that it is applicable to cases with large eccentricity due to the series converging quickly. This paper chooses the synchronous binary star V1143 Cyg that is on the main sequence and has a large eccentricity （e = 0.54） as an example calculation and gives the nu- merical results. Lastly, the evolutionary tendency including the evolution of orbit and spin, the time for the speed up of spin, the circularization time, the orbital collapse time and the life time are given in the discussion and conclusion. The results shown in this paper are an improvement on those from the previous paper.展开更多
In the era of big data,outsourcing massive data to a remote cloud server is a promising approach.Outsourcing storage and computation services can reduce storage costs and computational burdens.However,public cloud sto...In the era of big data,outsourcing massive data to a remote cloud server is a promising approach.Outsourcing storage and computation services can reduce storage costs and computational burdens.However,public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users.Privacy-preserving feature extraction techniques are an effective solution to this issue.Because the Rotation Invariant Local Binary Pattern(RILBP)has been widely used in various image processing fields,we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper(called PPRILBP).To protect image content,original images are encrypted using block scrambling,pixel circular shift,and pixel diffusion when uploaded to the cloud server.It is proved that RILBP features remain unchanged before and after encryption.Moreover,the server can directly extract RILBP features from encrypted images.Analyses and experiments confirm that the proposed scheme is secure and effective,and outperforms previous secure LBP feature computing methods.展开更多
基金supported by National Natural Science Foundation of China(No.61673283)
文摘This paper describes a brain-inspired simultaneous localization and mapping(SLAM)system using oriented features from accelerated segment test and rotated binary robust independent elementary(ORB)features of RGB(red,green,blue)sensor for a mobile robot.The core SLAM system,dubbed RatSLAM,can construct a cognitive map using information of raw odometry and visual scenes in the path traveled.Different from existing RatSLAM system which only uses a simple vector to represent features of visual image,in this paper,we employ an efficient and very fast descriptor method,called ORB,to extract features from RCB images.Experiments show that these features are suitable to recognize the sequences of familiar visual scenes.Thus,while loop closure errors are detected,the descriptive features will help to modify the pose estimation by driving loop closure and localization in a map correction algorithm.Efficiency and robustness of our method are also demonstrated by comparing with different visual processing algorithms.
文摘This paper provides a method to study the solution of equations for syn- chronous binary stars with large eccentricity on the main sequence. The theoretical results show that the evolution of the eccentricity is linear with time or follows an exponential form, and the semi-major axis and spin vary with time in an exponen- tial form that are different from the results given in a previous paper. The improved method is applicable in both cases of large eccentricity and small eccentricity. In ad- dition, the number of terms in the expansion of a series with small eccentricity is very long due to the series converging slowly. The advantage of this method is that it is applicable to cases with large eccentricity due to the series converging quickly. This paper chooses the synchronous binary star V1143 Cyg that is on the main sequence and has a large eccentricity （e = 0.54） as an example calculation and gives the nu- merical results. Lastly, the evolutionary tendency including the evolution of orbit and spin, the time for the speed up of spin, the circularization time, the orbital collapse time and the life time are given in the discussion and conclusion. The results shown in this paper are an improvement on those from the previous paper.
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.61872408the Natural Science Foundation of Hunan Province(2020JJ4238)+1 种基金the Social Science Fund of Hunan Province under Grant No.16YBA102the Research Fund of Hunan Provincial Key Laboratory of informationization technology for basic education under Grant No.2015TP1017.
文摘In the era of big data,outsourcing massive data to a remote cloud server is a promising approach.Outsourcing storage and computation services can reduce storage costs and computational burdens.However,public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users.Privacy-preserving feature extraction techniques are an effective solution to this issue.Because the Rotation Invariant Local Binary Pattern(RILBP)has been widely used in various image processing fields,we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper(called PPRILBP).To protect image content,original images are encrypted using block scrambling,pixel circular shift,and pixel diffusion when uploaded to the cloud server.It is proved that RILBP features remain unchanged before and after encryption.Moreover,the server can directly extract RILBP features from encrypted images.Analyses and experiments confirm that the proposed scheme is secure and effective,and outperforms previous secure LBP feature computing methods.
