Directionlet transform can capture the image singularity due to possessing the multi-direction anisotropic basis functions. A texture classification algorithm based on the Mapping complex Directionlet Transform (M-DT)...
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Directionlet transform can capture the image singularity due to possessing the multi-direction anisotropic basis functions. A texture classification algorithm based on the Mapping complex Directionlet Transform (M-DT) is proposed, which provides better directionality and approximate shift invariance. By space mapping for the texture image, then complex Directionlet transform is applied to the mapped image, and the multiscale subband coefficient energy feature is used for texture classification. The experiments using texture images from Brodatz and real SAR images indicate the proposed method outperforms wavelets and Multiscale Geometric Analysis (MGA) approaches, the potential application to image analysis by Directionlet is thus proved.
A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differenti...
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A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differential equation. A new function is proposed to characterize the population of excitatory and inhibitory spiking neurons, which is different from the population density function discussed by most researchers. And a novel evolution equation, which is a nonhomogeneous parabolic type equation, is derived. From this, the stationary solution and the firing rate of the stationary states are given. Last, by the Fourier transform, the time dependent solution is also obtained. This method can be used to analyze the various dynamic behaviors of neuronal populations.
This paper focuses on the image segmentation, which is one of the key problems in medical imageprocessing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information...
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Instrument display panel is one of the most important parts of automobiles. Automatic detection of LED signal lamps is critical to ensure the reliability of automobile systems. In this paper, an automatic detection me...
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This paper proposes a novel model, the mission oriented model, for the problem of land-based satellite tracking telemetry and command (TT&C) resources scheduling. Compared to other models, the mission oriented mod...
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This paper proposes a novel model, the mission oriented model, for the problem of land-based satellite tracking telemetry and command (TT&C) resources scheduling. Compared to other models, the mission oriented model constrains a satellite to be tracked and commanded by only a ground station which can observe the satellite. Therefore, the proposed model makes it possible that scheduling algorithms schedule TT&C resources to complete more missions. Then it proposes the clonal selection land-based satellite TT&C resources scheduling algorithm (CS_STT&CRSA) based on the mission oriented model and proves its global convergence in theory. The algorithm adopts a matrix coding scheme, which depends on the start times of tracked and commanded orbits and the relationships between satellites and ground stations. The severe-constraint satisfaction operator which guarantees the individual satisfies severe constraints is proposed. When there are 5 geostationary satellites and 30, 40 or 50 low earth orbit and medium earth orbit (LEO&MEO) satellites, 10 different groups of tasks are generated respectively. Experimental results illustrate that the mission oriented model enables scheduling algorithms to make better use of TT&C resources and complete more missions and CS_TT&CRSA has more powerful ability of searching and solving constraints and is more stable.
In this paper, a novel curve reconstruction method based on A* algorithm from a set of dense scattered points was proposed. Our method can not only reconstruct dense scattered points with single connected complicated ...
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How to make full use of the limited energy available and prolonging the survival time of node is a major concern in research on Wireless Sensor Networks (WSNs). This paper presented an improved approach based on Direc...
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ISBN:
(纸本)9780769538235
How to make full use of the limited energy available and prolonging the survival time of node is a major concern in research on Wireless Sensor Networks (WSNs). This paper presented an improved approach based on Directed Diffusion Protocol (DDP) which could meet the features of WSN, such as data-oriented, many-to-one transmissions and low energy costs, together with data aggregation mechanisms which emphasized on cascading timeouts, to eliminate the redundancy caused by the transmission of large amounts of data. This modified protocol prohibited the direct forwarding of central nodes, balancing data traffic of different sensors between the source and the sink, and proved to be able of reducing total network flows, energy consumptions and prolonging the lifetime of the network through simulations.
A new algorithm for constrained multi-objective optimization is presented. The algorithm treats the constraints as an objective and the immune clone and immune memory mechanism are introduced. Therefore, the new algor...
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A new algorithm for constrained multi-objective optimization is presented. The algorithm treats the constraints as an objective and the immune clone and immune memory mechanism are introduced. Therefore, the new algorithm could find the Pareto-optimal solutions from the feasible region and the edge of the infeasible region, which assures both the convergence and diversity of the obtained solutions. Simulation results show that the new algorithm has much better performance in finding a much better spread of solutions, in maintaining a better uniformity of the solutions and in obtaining a better convergence.
This paper proposes a novel inductive semi-supervised algorithm for web page classification named GCo-training, exploiting texts in web pages and hyperlinks among them. GCo-training iteratively trains two classifiers-...
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This paper proposes a novel inductive semi-supervised algorithm for web page classification named GCo-training, exploiting texts in web pages and hyperlinks among them. GCo-training iteratively trains two classifiers-a graph-based semi-supervised classifier based on hyperlinks among web pages and a Bayes classifier based on texts in web pages, under the framework of Co-training. On the one hand, the graph-based semi-supervised classifier obtains high accuracy based on a small set of labeled examples through exploiting links among web pages and can augment labeled examples for the Bayes classifier. On the other hand, the Bayes classifier can also provide labeled example for the graph-based classifier after it learning on labeled set augmented by the graph-based classifier. Therefore, the two classifiers help each other and improve their respective performance during the process of training. Finally, the Bayes classifier can classify a large number of unseen examples. We test GCo-training algorithm, Co-training algorithm based on words occurring on web pages and words occurring in hyperlinks and Bayes algorithm based on EM on the Web&KB dataset. Experimental results show GCo-training performs much better than the other algorithms.
It is significant to research the work safe and emergency management information system (PSEMIS) in large-scale hydroelectric project. This paper analyses the reviews and China Three Gorges Project Corporation work sa...
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