The notion of a communication channel is one of the key notions in information theory but like the notion “information” it has not any general mathematical definition. The existing examples of the communication chan...The notion of a communication channel is one of the key notions in information theory but like the notion “information” it has not any general mathematical definition. The existing examples of the communication channels: the Gaussian ones;the binary symmetric ones;the ones with symbol drop-out and drop-in;the ones with error packets etc., characterize the distortions which take place in information conducted through the corresponding channel.展开更多
The clustering of objects(individuals or variables)is one of the most used approaches to exploring multivariate data.The two most common unsupervised clustering strategies are hierarchical ascending clustering(HAC)and...The clustering of objects(individuals or variables)is one of the most used approaches to exploring multivariate data.The two most common unsupervised clustering strategies are hierarchical ascending clustering(HAC)and k-means partitioning used to identify groups of similar objects in a dataset to divide it into homogeneous groups.The proposed topological clustering of variables,called TCV,studies an homogeneous set of variables defined on the same set of individuals,based on the notion of neighborhood graphs,some of these variables are more-or-less correlated or linked according to the type quantitative or qualitative of the variables.This topological data analysis approach can then be useful for dimension reduction and variable selection.It’s a topological hierarchical clustering analysis of a set of variables which can be quantitative,qualitative or a mixture of both.It arranges variables into homogeneous groups according to their correlations or associations studied in a topological context of principal component analysis(PCA)or multiple correspondence analysis(MCA).The proposed TCV is adapted to the type of data considered,its principle is presented and illustrated using simple real datasets with quantitative,qualitative and mixed variables.The results of these illustrative examples are compared to those of other variables clustering approaches.展开更多
The whole procedures of underwater digital terrain model (DTM) were presented by building with the global positioning system (GPS) aided high-resolution profile-scan sonar images.The algorithm regards the digital imag...The whole procedures of underwater digital terrain model (DTM) were presented by building with the global positioning system (GPS) aided high-resolution profile-scan sonar images.The algorithm regards the digital image scanned in a cycle as the raw data.First the label rings are detected with the improved Hough transform (HT) method and followed by curve-fitting for accurate location;then the most probable window for each ping is detected with weighted neighborhood gray-level co-occurrence matrix;and finally the DTM is built by integrating the GPS data with sonar data for 3D visualization.The case of an underwater trench for immersed tube road tunnel is illustrated.展开更多
文摘The notion of a communication channel is one of the key notions in information theory but like the notion “information” it has not any general mathematical definition. The existing examples of the communication channels: the Gaussian ones;the binary symmetric ones;the ones with symbol drop-out and drop-in;the ones with error packets etc., characterize the distortions which take place in information conducted through the corresponding channel.
文摘The clustering of objects(individuals or variables)is one of the most used approaches to exploring multivariate data.The two most common unsupervised clustering strategies are hierarchical ascending clustering(HAC)and k-means partitioning used to identify groups of similar objects in a dataset to divide it into homogeneous groups.The proposed topological clustering of variables,called TCV,studies an homogeneous set of variables defined on the same set of individuals,based on the notion of neighborhood graphs,some of these variables are more-or-less correlated or linked according to the type quantitative or qualitative of the variables.This topological data analysis approach can then be useful for dimension reduction and variable selection.It’s a topological hierarchical clustering analysis of a set of variables which can be quantitative,qualitative or a mixture of both.It arranges variables into homogeneous groups according to their correlations or associations studied in a topological context of principal component analysis(PCA)or multiple correspondence analysis(MCA).The proposed TCV is adapted to the type of data considered,its principle is presented and illustrated using simple real datasets with quantitative,qualitative and mixed variables.The results of these illustrative examples are compared to those of other variables clustering approaches.
文摘The whole procedures of underwater digital terrain model (DTM) were presented by building with the global positioning system (GPS) aided high-resolution profile-scan sonar images.The algorithm regards the digital image scanned in a cycle as the raw data.First the label rings are detected with the improved Hough transform (HT) method and followed by curve-fitting for accurate location;then the most probable window for each ping is detected with weighted neighborhood gray-level co-occurrence matrix;and finally the DTM is built by integrating the GPS data with sonar data for 3D visualization.The case of an underwater trench for immersed tube road tunnel is illustrated.
