To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modi...To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.展开更多
The research deal with the reconstruction through digital 3D CAAD （Computer Aided Architectural Design） modeling of masterpieces of modem and contemporary architecture. The charm of reconstruction through digital mo...The research deal with the reconstruction through digital 3D CAAD （Computer Aided Architectural Design） modeling of masterpieces of modem and contemporary architecture. The charm of reconstruction through digital modeling is far less than that of work done on traditional maquette, indeed, makes much deeper level of detail and specificity from knowing. We had to know many technical characteristics of the buildings beyond size, like static-structural features, physical features, economic features and others. In this way＇ the model become real-simulation, a simulated architectural model in all aspects. In addition to these aspects we deeply analyze also the formal, morphological, historical and architectural aspects. The idea is to revitalize and re-discover the logics and the rules of the projected constructions that the designer architect have invented for each masterpiece of architecture, through the comprehension of how is done. The proportional analysis of the modularity on which the design is based is mandatory subject of investigation.展开更多
Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel se...Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel segmentation.A saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.In the left structure,the first layer is upsampling and the other layers are max-pooling.In the right structure,the final layer is max-pooling and the other layers are upsampling.The proposed approach is evaluated using the DRIVE database.Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models.The proposed algorithm performs well in extracting vessel networks,and is more accurate than other deep learning methods.Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.展开更多
基金supported in part by the National Natural Science Foundation of China (NO. 61401004, 61271233, 60972038)Plan of introduction and cultivation of university leading talents in Anhui (No.gxfxZ D2016013)+3 种基金the Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (No. KJ2010B357)Startup Project of Anhui Normal University Doctor Scientific Research (No.2016XJJ129)the US National Science Foundation under grants CNS1702957 and ACI-1642133the Wireless Engineering Research and Education Center at Auburn University
文摘To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.
文摘The research deal with the reconstruction through digital 3D CAAD （Computer Aided Architectural Design） modeling of masterpieces of modem and contemporary architecture. The charm of reconstruction through digital modeling is far less than that of work done on traditional maquette, indeed, makes much deeper level of detail and specificity from knowing. We had to know many technical characteristics of the buildings beyond size, like static-structural features, physical features, economic features and others. In this way＇ the model become real-simulation, a simulated architectural model in all aspects. In addition to these aspects we deeply analyze also the formal, morphological, historical and architectural aspects. The idea is to revitalize and re-discover the logics and the rules of the projected constructions that the designer architect have invented for each masterpiece of architecture, through the comprehension of how is done. The proportional analysis of the modularity on which the design is based is mandatory subject of investigation.
基金Project supported by the Natural Science Foundation of Fujian Province,China(No.2016J0129)the Educational Commission of Fujian Province of China(No.JAT170180)
文摘Retinal vessel segmentation is a significant problem in the analysis of fundus images.A novel deep learning structure called the Gaussian net(GNET)model combined with a saliency model is proposed for retinal vessel segmentation.A saliency image is used as the input of the GNET model replacing the original image.The GNET model adopts a bilaterally symmetrical structure.In the left structure,the first layer is upsampling and the other layers are max-pooling.In the right structure,the final layer is max-pooling and the other layers are upsampling.The proposed approach is evaluated using the DRIVE database.Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models.The proposed algorithm performs well in extracting vessel networks,and is more accurate than other deep learning methods.Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.
