Topological entropy can be an indicator of complicated behavior in dynamical systems. It is first introduce by Adler, Konheim and McAndrew by using open covers in 1965. After that it is still an active research by man...Topological entropy can be an indicator of complicated behavior in dynamical systems. It is first introduce by Adler, Konheim and McAndrew by using open covers in 1965. After that it is still an active research by many researchers to produce more properties and applications up to nowadays. The purpose of this paper is to review and explain most important concepts and results of topological entropies of continuous self-maps for dynamical systems on compact and non-compact topological and metric spaces. We give proofs for some of its elementary properties of the topological entropy. Slight modification on Adler's topological entropy is also presented.展开更多
Generalized Space Shift Keying (GSSK) modulation is a low-complexity spatial nmltiplexing technique for nmltiple-antenna wireless systems. However, effective transmit antenna combinations have to be preselected, and...Generalized Space Shift Keying (GSSK) modulation is a low-complexity spatial nmltiplexing technique for nmltiple-antenna wireless systems. However, effective transmit antenna combinations have to be preselected, and there exist redundant antenna combinations which are not used in GSSK. In this paper, a novel adaptive mapping scheme for GSSK modulation, named as Adaptive Mapping Generalized Space Shift Keying (AMGSSK), is presented. Compared with GSSK, the antenna combinations are updated adaptively according to the Channel State Inforrmtion (CSI) in the proposed AMGSSK system, and the perfonrance of average Symbol Error Rate (SER) is reduced considerably. In the proposed scheme, two algorithrrs for selecting the optimum antenna combinations are described. The SER perfonmnce of AMGSSK is analyzed theoretically, and validated by Monte Carlo sinmlation. It is shown that the proposed AMGSSK scheme has good perfonmnce in SER and spectral efficiency.展开更多
Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods.However,recent dual clustering research has often omitted spatial out...Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods.However,recent dual clustering research has often omitted spatial outliers,subjectively determined the weights of hybrid distance measures,and produced diverse clustering results.In this study,we first redefined the dual clustering problem and related concepts to highlight the clustering criteria.We then presented a self-organizing dual clustering algorithm (SDC) based on the self-organizing feature map and certain spatial analysis operations,including the Voronoi diagram and polygon aggregation and amalgamation.The algorithm employs a hybrid distance measure that combines geometric distance and non-spatial similarity,while the clustering spectrum analysis helps to determine the weight of non-spatial similarity in the measure.A case study was conducted on a spatial database of urban land price samples in Wuhan,China.SDC detected spatial outliers and clustered the points into spatially connective and attributively homogenous sub-groups.In particular,SDC revealed zonal areas that describe the actual distribution of land prices but were not demonstrated by other methods.SDC reduced the subjectivity in dual clustering.展开更多
文摘Topological entropy can be an indicator of complicated behavior in dynamical systems. It is first introduce by Adler, Konheim and McAndrew by using open covers in 1965. After that it is still an active research by many researchers to produce more properties and applications up to nowadays. The purpose of this paper is to review and explain most important concepts and results of topological entropies of continuous self-maps for dynamical systems on compact and non-compact topological and metric spaces. We give proofs for some of its elementary properties of the topological entropy. Slight modification on Adler's topological entropy is also presented.
基金supported partially by the National Key Basic Research Program of China under Grant No.2007CB310605the Science and Technology Development Fund of Tianjin Colleges and Universities under Grant No.20080708the Research Fund of Tianjin University of Technology and Education under Grants No.KJ09-012,No.KJ10-06
文摘Generalized Space Shift Keying (GSSK) modulation is a low-complexity spatial nmltiplexing technique for nmltiple-antenna wireless systems. However, effective transmit antenna combinations have to be preselected, and there exist redundant antenna combinations which are not used in GSSK. In this paper, a novel adaptive mapping scheme for GSSK modulation, named as Adaptive Mapping Generalized Space Shift Keying (AMGSSK), is presented. Compared with GSSK, the antenna combinations are updated adaptively according to the Channel State Inforrmtion (CSI) in the proposed AMGSSK system, and the perfonrance of average Symbol Error Rate (SER) is reduced considerably. In the proposed scheme, two algorithrrs for selecting the optimum antenna combinations are described. The SER perfonmnce of AMGSSK is analyzed theoretically, and validated by Monte Carlo sinmlation. It is shown that the proposed AMGSSK scheme has good perfonmnce in SER and spectral efficiency.
基金supported by the National Natural Science Foundation of China(Grant No.40901188)the Key Laboratory of Geo-informatics of the State Bureau of Surveying and Mapping(Grant No.200906)the Fundamental Research Funds for the Central Universities(Grant No.4082002)
文摘Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods.However,recent dual clustering research has often omitted spatial outliers,subjectively determined the weights of hybrid distance measures,and produced diverse clustering results.In this study,we first redefined the dual clustering problem and related concepts to highlight the clustering criteria.We then presented a self-organizing dual clustering algorithm (SDC) based on the self-organizing feature map and certain spatial analysis operations,including the Voronoi diagram and polygon aggregation and amalgamation.The algorithm employs a hybrid distance measure that combines geometric distance and non-spatial similarity,while the clustering spectrum analysis helps to determine the weight of non-spatial similarity in the measure.A case study was conducted on a spatial database of urban land price samples in Wuhan,China.SDC detected spatial outliers and clustered the points into spatially connective and attributively homogenous sub-groups.In particular,SDC revealed zonal areas that describe the actual distribution of land prices but were not demonstrated by other methods.SDC reduced the subjectivity in dual clustering.