The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Net...The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.展开更多
Cooperative spectrum sensing in cognitive radio is investigated to improve the detection performance of Primary User(PU).Meanwhile,cluster-based hierarchical cooperation is introduced for reducing the overhead as well...Cooperative spectrum sensing in cognitive radio is investigated to improve the detection performance of Primary User(PU).Meanwhile,cluster-based hierarchical cooperation is introduced for reducing the overhead as well as maintaining a certain level of sensing performance.However,in existing hierarchically cooperative spectrum sensing algorithms,the robustness problem of the system is seldom considered.In this paper,we propose a reputation-based hierarchically cooperative spectrum sensing scheme in Cognitive Radio Networks(CRNs).Before spectrum sensing,clusters are grouped based on the location correlation coefficients of Secondary Users(SUs).In the proposed scheme,there are two levels of cooperation,the first one is perfonned within a cluster and the second one is carried out among clusters.With the reputation mechanism and modified MAJORITY rule in the second level cooperation,the proposed scheme can not only relieve the influence of the shadowing,but also eliminate the impact of the PU emulation attack on a relatively large scale.Simulation results show that,in the scenarios with deep-shadowing or multiple attacked SUs,our proposed scheme achieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.展开更多
The integrated absorption cross section Σ abs, peak emis sion cross section σ emi, Judd-Ofeld intensity parameters Ω t(t=2,4,6), and spontaneous emission probability A R of Er 3+ ions were determined fo r...The integrated absorption cross section Σ abs, peak emis sion cross section σ emi, Judd-Ofeld intensity parameters Ω t(t=2,4,6), and spontaneous emission probability A R of Er 3+ ions were determined fo r Erbium doped alkali and alkaline earth phosphate glasses. It is found the comp ositional dependence of σ emi is almost similar to that of Σ abs, wh ich is determined by the sum of Ω t (3Ω 2+10Ω 4+21Ω 6). In addition, the compositional dependence of Ω t was studied in these glass systems. As a resu lt, compared with Ω 4 and Ω 6, the Ω 2 has a stronger compositional depend ence on the ionic radius and content of modifiers. The covalency of Er-O bonds in phosphate glass is weaker than that in silicate glass, germanate glass, alumi nate glass, and tellurate glass, since Ω 6 of phosphate glass is relatively la rge. A R is affected by the covalency of the Er 3+ ion sites and correspon ds to the Ω 6 value.展开更多
We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupl...We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupling strength. The results show that we can easily control the dying state effectively by a randomly attacked situation. We then investigate the recovery activity of the networks by increasing the inter-layer coupled strength. The optimal values of the inter-layer coupled strengths are found, which could provide a more effective range to recovery activity of complex networks. As the multilayer systems composed of active and inactive elements raise important and interesting problems, our results on the target inactivation and recovery in two-layer networks would be extended to different studies.展开更多
目的基于网络药理学和分子对接技术探究苍耳子散(CRZS)治疗异质性鼻炎的作用机制以及“多成分-多靶点-多通路”的整体药理作用特征。方法利用中药系统药理学数据库与分析平台(TCMSP)检索CRZS的活性成分及靶点;从GeneCards、DisGeNET、Ph...目的基于网络药理学和分子对接技术探究苍耳子散(CRZS)治疗异质性鼻炎的作用机制以及“多成分-多靶点-多通路”的整体药理作用特征。方法利用中药系统药理学数据库与分析平台(TCMSP)检索CRZS的活性成分及靶点;从GeneCards、DisGeNET、Phenopedia、TTD和PharmGKB数据库获取相关鼻炎的基因。通过STRING数据库与Cytoscape软件构建蛋白质-蛋白质相互作用(PPI)网络;利用STRING数据库对核心基因进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。通过Cytoscape软件构建“中药成分-靶点-信号通路”网络并筛选核心靶点;采用AutoDock软件对筛选得到的关键通路靶点进行分子对接。结果CRZS有131个核心基因,鼻炎相关靶点756个,交集靶点41个。GO生物富集条目2005条,KEGG通路富集条目151条,HIF-1、Lipid and atherosclerosis、Fluid shear stress and atherosclerosis等通路最具连接可能。分子对接显示,CEZS靶点的有效成分与3个核心基因连接紧密。结论CEZS可通过多成分、多靶点,多信号通路发挥治疗鼻炎的作用,其中最有可能通过HIF-1、lipid and atherosclerosis、Fluid shear stress and atherosclerosis等3条通路进行调控。展开更多
Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and...Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,etc.As a segment of the clever transportation structure,the acknowledgment of traffic signs is basic for the driving assistance system,traffic sign upkeep,self-administering driving,and various spaces.There are different assessments turns out achieved for traffic sign acknowledgment in the world.However,most of the works are only for explicit arrangements of traffic signs,for example,beyond what many would consider a possible sign.Traffic sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of traffic sign pictures.Two critical issues exist during the time spent identification(ID)and affirmation of traffic signals.Road signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road signs.Also concealing information from traffic scene pictures is affected by moving light achieved by environment conditions,time(day-night),and shadowing.Traffic sign revelation and affirmation structure has two guideline sorts out:The essential stage incorpo-rates the traffic sign limitation and the resulting stage portrays the perceived traffic signs into a particular class.展开更多
Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including we...Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including weather con-ditions,soil qualities,water levels and the location of the farm.A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting.The combination of data mining and deep learning creates a whole crop yield pre-diction system that is able to connect raw data to predicted crop yields.The sug-gested study uses a Discrete Deep belief network with Visual Geometry Group(VGG)Net classification method over the tweak chick swarm optimization approach to estimate agricultural production.The Network’s successively stacked layers were fed the data parameters.Based on the input parameters,a crop produc-tion prediction environment is constructed using the network architecture.Using the tweak chick swarm optimization technique,the best characteristics of input data are preprocessed,and the optimal output is used as input for the classification process.Discrete Deep belief network with the Visual Geometry Group Net clas-sifier is used to classify the data and forecast agricultural production.The sug-gested model correctly predicts crop output with 97 percent accuracy,exceeding existing models by maintaining the baseline data distribution.展开更多
文摘The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.
基金ACKNOWLEDGEMENT This work was partially supported by the Na- tional Natural Science Foundation of China under Grant No. 61071127 and the Science and Technology Department of Zhejiang Pro- vince under Grants No. 2012C01036-1, No. 2011R10035.
文摘Cooperative spectrum sensing in cognitive radio is investigated to improve the detection performance of Primary User(PU).Meanwhile,cluster-based hierarchical cooperation is introduced for reducing the overhead as well as maintaining a certain level of sensing performance.However,in existing hierarchically cooperative spectrum sensing algorithms,the robustness problem of the system is seldom considered.In this paper,we propose a reputation-based hierarchically cooperative spectrum sensing scheme in Cognitive Radio Networks(CRNs).Before spectrum sensing,clusters are grouped based on the location correlation coefficients of Secondary Users(SUs).In the proposed scheme,there are two levels of cooperation,the first one is perfonned within a cluster and the second one is carried out among clusters.With the reputation mechanism and modified MAJORITY rule in the second level cooperation,the proposed scheme can not only relieve the influence of the shadowing,but also eliminate the impact of the PU emulation attack on a relatively large scale.Simulation results show that,in the scenarios with deep-shadowing or multiple attacked SUs,our proposed scheme achieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.
基金Funded by the Natural Science Foundation of Guangdong Prov ince(013013) and the Science and Technology Plan of Guangdong Province(2002B11604)
文摘The integrated absorption cross section Σ abs, peak emis sion cross section σ emi, Judd-Ofeld intensity parameters Ω t(t=2,4,6), and spontaneous emission probability A R of Er 3+ ions were determined fo r Erbium doped alkali and alkaline earth phosphate glasses. It is found the comp ositional dependence of σ emi is almost similar to that of Σ abs, wh ich is determined by the sum of Ω t (3Ω 2+10Ω 4+21Ω 6). In addition, the compositional dependence of Ω t was studied in these glass systems. As a resu lt, compared with Ω 4 and Ω 6, the Ω 2 has a stronger compositional depend ence on the ionic radius and content of modifiers. The covalency of Er-O bonds in phosphate glass is weaker than that in silicate glass, germanate glass, alumi nate glass, and tellurate glass, since Ω 6 of phosphate glass is relatively la rge. A R is affected by the covalency of the Er 3+ ion sites and correspon ds to the Ω 6 value.
基金Supported by the National Basic Research Program of China under Grant Nos 2013CBA01502,2011CB921503 and 2013CB834100the National Natural Science Foundation of China under Grant Nos 11374040 and 11274051
文摘We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupling strength. The results show that we can easily control the dying state effectively by a randomly attacked situation. We then investigate the recovery activity of the networks by increasing the inter-layer coupled strength. The optimal values of the inter-layer coupled strengths are found, which could provide a more effective range to recovery activity of complex networks. As the multilayer systems composed of active and inactive elements raise important and interesting problems, our results on the target inactivation and recovery in two-layer networks would be extended to different studies.
文摘提出了一种2.5维(2.5D)系统封装高速输入/输出(I/O)全链路的信号/电源完整性(Signal integrity/power integrity,SI/PI)协同仿真方法。首先通过电磁全波仿真分析SiP内部“芯片I/O引脚-有源转接板-印刷电路板(即封装基板)-封装体I/O引脚”这一主要高速信号链路及相应的转接板/印刷电路板电源分配网络(Power distribution network,PDN)的结构特征和电学特性,在此基础上分别搭建对应有源转接板和印刷电路板两种组装层级的“信号链路+PDN”模型,并分别进行SI/PI协同仿真,提取出反映信号链路/PDN耦合特性的模块化集总电路模型,从而在电路仿真器中以级联模型实现快速的SI/PI协同仿真。与全链路的全波仿真结果的对比表明,模块化后的协同仿真有很好的可信度,而且仿真时间与资源开销大幅缩减,效率明显提升。同时总结了去耦电容的大小与布局密度对PDN电源完整性的影响及对信号完整性的潜在影响,提出了去耦电容布局优化的建议。
文摘目的基于网络药理学和分子对接技术探究苍耳子散(CRZS)治疗异质性鼻炎的作用机制以及“多成分-多靶点-多通路”的整体药理作用特征。方法利用中药系统药理学数据库与分析平台(TCMSP)检索CRZS的活性成分及靶点;从GeneCards、DisGeNET、Phenopedia、TTD和PharmGKB数据库获取相关鼻炎的基因。通过STRING数据库与Cytoscape软件构建蛋白质-蛋白质相互作用(PPI)网络;利用STRING数据库对核心基因进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。通过Cytoscape软件构建“中药成分-靶点-信号通路”网络并筛选核心靶点;采用AutoDock软件对筛选得到的关键通路靶点进行分子对接。结果CRZS有131个核心基因,鼻炎相关靶点756个,交集靶点41个。GO生物富集条目2005条,KEGG通路富集条目151条,HIF-1、Lipid and atherosclerosis、Fluid shear stress and atherosclerosis等通路最具连接可能。分子对接显示,CEZS靶点的有效成分与3个核心基因连接紧密。结论CEZS可通过多成分、多靶点,多信号通路发挥治疗鼻炎的作用,其中最有可能通过HIF-1、lipid and atherosclerosis、Fluid shear stress and atherosclerosis等3条通路进行调控。
文摘Traffic signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,etc.As a segment of the clever transportation structure,the acknowledgment of traffic signs is basic for the driving assistance system,traffic sign upkeep,self-administering driving,and various spaces.There are different assessments turns out achieved for traffic sign acknowledgment in the world.However,most of the works are only for explicit arrangements of traffic signs,for example,beyond what many would consider a possible sign.Traffic sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of traffic sign pictures.Two critical issues exist during the time spent identification(ID)and affirmation of traffic signals.Road signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road signs.Also concealing information from traffic scene pictures is affected by moving light achieved by environment conditions,time(day-night),and shadowing.Traffic sign revelation and affirmation structure has two guideline sorts out:The essential stage incorpo-rates the traffic sign limitation and the resulting stage portrays the perceived traffic signs into a particular class.
文摘Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including weather con-ditions,soil qualities,water levels and the location of the farm.A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting.The combination of data mining and deep learning creates a whole crop yield pre-diction system that is able to connect raw data to predicted crop yields.The sug-gested study uses a Discrete Deep belief network with Visual Geometry Group(VGG)Net classification method over the tweak chick swarm optimization approach to estimate agricultural production.The Network’s successively stacked layers were fed the data parameters.Based on the input parameters,a crop produc-tion prediction environment is constructed using the network architecture.Using the tweak chick swarm optimization technique,the best characteristics of input data are preprocessed,and the optimal output is used as input for the classification process.Discrete Deep belief network with the Visual Geometry Group Net clas-sifier is used to classify the data and forecast agricultural production.The sug-gested model correctly predicts crop output with 97 percent accuracy,exceeding existing models by maintaining the baseline data distribution.
