A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-...A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.The Gini index(GI)has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood,Scaling,Rising Tide,Cloning,Bill Gates,and Babies.By combining the total variation(TV)operator,the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed.In addition,the corresponding algorithm based on a primal-dual framework is also proposed.Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.展开更多
City size distribution is of interest because of a number of key stylized facts, including notably Zipf's law for cities and the importance of urban primacy. But a new and more efficient method Gini index can be u...City size distribution is of interest because of a number of key stylized facts, including notably Zipf's law for cities and the importance of urban primacy. But a new and more efficient method Gini index can be used for calculating regional city size distribution. This paper begins by developing a calculation method for the Gini index, dividing the whole country into 26 areas and then calculating each area's Gini index value. Based on these calculation results, this paper gives a preliminary study on regional differences of its city size distribution and the dynamics.展开更多
In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose...In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar(ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation(SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio(PSLR) and the reconstruction relative error(RE) indicate that the proposed method outperforms the l1 norm based method.展开更多
Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement ...Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems.Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud.This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system.Wireless sensor nodes are used to monitor the biosignals dynamically.All accrued data is temporarily stored in the dew layer.It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data.Data synchronization plays an essential role in the cloud dew framework.We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud.Sometimes sensors produce erroneous data,which poses a significant threat to the sustainable health monitoring system.Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers.Further,we compared the proposed MedGini model with the existing models and analyzed the system performance.MedGini is found to outperform others concerning cost and power consumption.展开更多
The Gini-Simpson quadratic index is a classic measure of diversity, widely used by ecologists. As shown recently, however, this index is not suitable for the measurement of beta diversity when the number of species is...The Gini-Simpson quadratic index is a classic measure of diversity, widely used by ecologists. As shown recently, however, this index is not suitable for the measurement of beta diversity when the number of species is very large. The objective of this paper is to introduce the Rich- Gini-Simpson quadratic index which preserves all the qualities of the classic Gini-Simpson index but behaves very well even when the number of species is very large. The additive partitioning of species diversity using the Rich-Gini- Simpson quadratic index and an application from island biogeography are analyzed.展开更多
The weighted Gini-Simpson quadratic index is the simplest measure of biodiversity which takes into account the relative abundance of species and some weights assigned to the species. These weights could be assigned ba...The weighted Gini-Simpson quadratic index is the simplest measure of biodiversity which takes into account the relative abundance of species and some weights assigned to the species. These weights could be assigned based on factors such as the phylogenetic distance between species, or their relative conservation values, or even the species richness or vulnerability of the habitats where these species live. In the vast majority of cases where the biodiversity is measured the species are supposed to be independent, which means that the relative proportion of a pair of species is the product of the relative proportions of the component species making up the respective pair. In the first section of the paper, the main versions of the weighted Gini-Simpson index of biodiversity for the pairs and triads of independent species are presented. In the second section of the paper, the weighted Gini-Simpson quadratic index is calculated for the general case when the species are interdependent. In this instance, the weights reflect the conservation values of the species and the distribution pattern variability of the subsets of species in the respective habitat induced by the inter-dependence between species. The third section contains a numerical example.展开更多
The number and composition of species in a community can be quantified withα-diversity indices,including species richness(R),Simpson’s index(D),and the Shannon-Wiener index(H΄).In forest communities,there are large ...The number and composition of species in a community can be quantified withα-diversity indices,including species richness(R),Simpson’s index(D),and the Shannon-Wiener index(H΄).In forest communities,there are large variations in tree size among species and individu-als of the same species,which result in differences in eco-logical processes and ecosystem functions.However,tree size inequality(TSI)has been largely neglected in studies using the available diversity indices.The TSI in the diameter at breast height(DBH)data for each of 99920 m×20 m forest census quadrats was quantified using the Gini index(GI),a measure of the inequality of size distribution.The generalized performance equation was used to describe the rotated and right-shifted Lorenz curve of the cumulative proportion of DBH and the cumulative proportion of number of trees per quadrat.We also examined the relationships ofα-diversity indices with the GI using correlation tests.The generalized performance equation effectively described the rotated and right-shifted Lorenz curve of DBH distributions,with most root-mean-square errors(990 out of 999 quadrats)being<0.0030.There were significant positive correlations between each of threeα-diversity indices(i.e.,R,D,and H’)and the GI.Nevertheless,the total abundance of trees in each quadrat did not significantly influence the GI.This means that the TSI increased with increasing spe-cies diversity.Thus,two new indices are proposed that can balanceα-diversity against the extent of TSI in the com-munity:(1−GI)×D,and(1−GI)×H’.These new indices were significantly correlated with the original D and H΄,and did not increase the extent of variation within each group of indices.This study presents a useful tool for quantifying both species diversity and the variation in tree sizes in forest communities,especially in the face of cumulative species loss under global climate change.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants No.62027803,No.61601096,No.61971111,No.61801089,and No.61701095in part by the Science and Technology Program under Grants No.8091C24,No.80904020405,No.2021JCJQJJ0949,and No.2022JCJQJJ0784in part by Industrial Technology Development Program under Grant No.2020110C041.
文摘A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.The Gini index(GI)has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood,Scaling,Rising Tide,Cloning,Bill Gates,and Babies.By combining the total variation(TV)operator,the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed.In addition,the corresponding algorithm based on a primal-dual framework is also proposed.Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.
文摘City size distribution is of interest because of a number of key stylized facts, including notably Zipf's law for cities and the importance of urban primacy. But a new and more efficient method Gini index can be used for calculating regional city size distribution. This paper begins by developing a calculation method for the Gini index, dividing the whole country into 26 areas and then calculating each area's Gini index value. Based on these calculation results, this paper gives a preliminary study on regional differences of its city size distribution and the dynamics.
基金supported by National Natural Science Foundationof China(Nos.61071146,61171165 and 61301217)Natural ScienceFoundation of Jiangsu Province(No.BK2010488)National Scientific Equipment Developing Project of China(No.2012YQ050250)
文摘In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar(ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation(SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio(PSLR) and the reconstruction relative error(RE) indicate that the proposed method outperforms the l1 norm based method.
文摘Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems.Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud.This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system.Wireless sensor nodes are used to monitor the biosignals dynamically.All accrued data is temporarily stored in the dew layer.It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data.Data synchronization plays an essential role in the cloud dew framework.We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud.Sometimes sensors produce erroneous data,which poses a significant threat to the sustainable health monitoring system.Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers.Further,we compared the proposed MedGini model with the existing models and analyzed the system performance.MedGini is found to outperform others concerning cost and power consumption.
文摘The Gini-Simpson quadratic index is a classic measure of diversity, widely used by ecologists. As shown recently, however, this index is not suitable for the measurement of beta diversity when the number of species is very large. The objective of this paper is to introduce the Rich- Gini-Simpson quadratic index which preserves all the qualities of the classic Gini-Simpson index but behaves very well even when the number of species is very large. The additive partitioning of species diversity using the Rich-Gini- Simpson quadratic index and an application from island biogeography are analyzed.
文摘The weighted Gini-Simpson quadratic index is the simplest measure of biodiversity which takes into account the relative abundance of species and some weights assigned to the species. These weights could be assigned based on factors such as the phylogenetic distance between species, or their relative conservation values, or even the species richness or vulnerability of the habitats where these species live. In the vast majority of cases where the biodiversity is measured the species are supposed to be independent, which means that the relative proportion of a pair of species is the product of the relative proportions of the component species making up the respective pair. In the first section of the paper, the main versions of the weighted Gini-Simpson index of biodiversity for the pairs and triads of independent species are presented. In the second section of the paper, the weighted Gini-Simpson quadratic index is calculated for the general case when the species are interdependent. In this instance, the weights reflect the conservation values of the species and the distribution pattern variability of the subsets of species in the respective habitat induced by the inter-dependence between species. The third section contains a numerical example.
基金supported by the National Natural Science Foundation of China(32101260).
文摘The number and composition of species in a community can be quantified withα-diversity indices,including species richness(R),Simpson’s index(D),and the Shannon-Wiener index(H΄).In forest communities,there are large variations in tree size among species and individu-als of the same species,which result in differences in eco-logical processes and ecosystem functions.However,tree size inequality(TSI)has been largely neglected in studies using the available diversity indices.The TSI in the diameter at breast height(DBH)data for each of 99920 m×20 m forest census quadrats was quantified using the Gini index(GI),a measure of the inequality of size distribution.The generalized performance equation was used to describe the rotated and right-shifted Lorenz curve of the cumulative proportion of DBH and the cumulative proportion of number of trees per quadrat.We also examined the relationships ofα-diversity indices with the GI using correlation tests.The generalized performance equation effectively described the rotated and right-shifted Lorenz curve of DBH distributions,with most root-mean-square errors(990 out of 999 quadrats)being<0.0030.There were significant positive correlations between each of threeα-diversity indices(i.e.,R,D,and H’)and the GI.Nevertheless,the total abundance of trees in each quadrat did not significantly influence the GI.This means that the TSI increased with increasing spe-cies diversity.Thus,two new indices are proposed that can balanceα-diversity against the extent of TSI in the com-munity:(1−GI)×D,and(1−GI)×H’.These new indices were significantly correlated with the original D and H΄,and did not increase the extent of variation within each group of indices.This study presents a useful tool for quantifying both species diversity and the variation in tree sizes in forest communities,especially in the face of cumulative species loss under global climate change.
