In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the model...In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.展开更多
Energy is the determinant factor for the survival of Mobile Sensor Networks(MSN).Based on the analysis of the energy distribution in this paper,a two-phase relocation algorithm is proposed based on the balance between...Energy is the determinant factor for the survival of Mobile Sensor Networks(MSN).Based on the analysis of the energy distribution in this paper,a two-phase relocation algorithm is proposed based on the balance between the energy provision and energy consumption distribution.Our main objectives are to maximize the coverage percentage and to minimize the total distance of node movements.This algorithm is designed to meet the requirement of non-uniform distribution network applications,to extend the lifetime of MSN and to simplify the design of the routing protocol.In ad-dition,test results show the feasibility of our proposed relocation algorithm.展开更多
Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are s...Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.展开更多
A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many up...A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.展开更多
文摘In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.
文摘Energy is the determinant factor for the survival of Mobile Sensor Networks(MSN).Based on the analysis of the energy distribution in this paper,a two-phase relocation algorithm is proposed based on the balance between the energy provision and energy consumption distribution.Our main objectives are to maximize the coverage percentage and to minimize the total distance of node movements.This algorithm is designed to meet the requirement of non-uniform distribution network applications,to extend the lifetime of MSN and to simplify the design of the routing protocol.In ad-dition,test results show the feasibility of our proposed relocation algorithm.
文摘Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.
基金supported by National Basic Research Program of China (No. 2010CB731800)National Natural Science Foundation of China (No. 60934003)Educational Foundation of Hebei Province (No. 2008147)
文摘A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.
