To understand the status of phytoplankton community of the Genhe River in the summer of 2015,we investigated the phytoplankton in Genhe River. We identified 5 phyla and 36 species,among which Bacillariophyta(23) were ...To understand the status of phytoplankton community of the Genhe River in the summer of 2015,we investigated the phytoplankton in Genhe River. We identified 5 phyla and 36 species,among which Bacillariophyta(23) were the most,followed by Chlorophyta(10),Cyanophyta(1),Chrysophyta(1),Pyrrophyta(1). The phytoplankton abundance was(15. 6-810) × 104 ind·L^(-1); the biomass was(0. 07-2. 876) mg·L-1; Shannon-wiener index was 1. 05-3. 24; Pielou evenness index was 0. 27-0. 96. Using Shannon-wiener index and Pielou index,the water quality of Genhe River was assessed,and the results showed that the water quality was the best at 5# sampling point,the water quality was good in 3#,4#,7#,8#,9# sampling points,and there was a state of pollution at other sampling points. Canonical correspondence analysis and Pearson correlation analysis showed that iron ion,transparency,p H value,water depth and water temperature were important environmental factors that affect the distribution of phytoplankton,and copper ion,nitrite ion and COD also significantly affected the distribution of phytoplankton.展开更多
A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based o...A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.展开更多
Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based ...Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.展开更多
Ecological geographic regions, also called eco-regions, can be used to divide a remotely sensed image, which is helpful for reducing the complexity of land cover types within eco-regions and for improving the classifi...Ecological geographic regions, also called eco-regions, can be used to divide a remotely sensed image, which is helpful for reducing the complexity of land cover types within eco-regions and for improving the classification accuracy of land cover. In this case study in China, we improved a method of ecological geographic regionalization that is more suitable for remote sensing mapping of regional land cover, and we obtained new eco-regions. The canonical correspondence analysis(CCA) and k-means clustering were adopted in the ecological geographic regionalization using both seasonal remotely-sensed vegetation information and environmental data including climate, elevation and soil features. Our results show that the combination of seasonal vegetation information and the CCA performed well in the selection of the dominant environmental factor of the biogeographic pattern, and it can be used as regionalization indicators of eco-regions. We found that thermal factors are the most important driving forces of the biogeographic pattern in China, which followed by moisture factors. Two global land cover products(MODIS MCD12C1 and Glob Cover) were used to assess our eco-regions. The results show that our eco-regions performed better than that of a previous study regarding the complexity of land cover types, such as in the number of types and the proportional area of the major/secondary type. These results indicate that the method of ecological geographic regionalization, which is based on environmental factors associated with seasonal vegetation features, is effective for reducing the regional complexity of land cover.展开更多
103 surface sediment samples in 71 water bodies, such as lakes with different salinity, swamps, shallow puddles and rivers on the Qinghai-Tibetan Plateau （QTP）, were collected to study the ecological distribution of...103 surface sediment samples in 71 water bodies, such as lakes with different salinity, swamps, shallow puddles and rivers on the Qinghai-Tibetan Plateau （QTP）, were collected to study the ecological distribution of living ostracods and their environmental implications. Total of 12 genus and 45 species living ostracods are identified. According to the frequencies and abundance of ostracods occurrence, Limnocythere dubiosa, Limnocytherellina kunlunensis, llyocypris bradyi, Candona candida, Eucypris rischtanica and Leucocythere dilatata are the common species on the QTP, with occurrence frequency of more than 8 and abundance of more than 570 in the 71 water bodies. Among them, L. dubiosa, occurring in 28 water bodies with 2177 shells, is the most widely distributed ostracod in this research. Canonical Correspondence Analysis （CCA） indicates high correlation between species and environmental variables, suggesting that the occurrence of species is strongly related to the changes in ecological conditions of habitats. Among eight environmental factors, salinity and pH value are the most affective variables that influence the species occurrence. L. kunlunensis is positively correlated with salinity while E. rischtanica is negatively correlated with salinity. C. candida has a positive correlation with salinity, as does I. bradyi although there is not such a strong correlation. L. dubiosa displays a positive correlation with pH value. Consequently, we discuss the environmental implications of the common living ostracods on the QTP based on the CCA as well as the distribution of ostracod species in different salinity and pH values water. L. dubiosa, L. kunlunensis and E. rischtanica are euryhaline species, among which, L. dubiosa is the most adaptable species on the QTP with large occurrence in sundry salinity water and the most widely adaptive range for pH values. L. kunlunensis prefers to saline water while E. rischtanica prefers to fresh water. Both L. kunlunensis and E. rischtanica can live in water from faintly acid to alkaline, in contrast, L. dubiosa only appears in neutral and alkaline water bodies. L bradyi only occurs in fresh water and oligohaline water with a large pH tolerance range tolerance range from weakly acidic water to alkaline water weakly acidic water to alkaline water. C. Candida lives in freshwater, with pH value above eight. The six common species reach maximum abundance in alkaline water （pH 8-10） except for llyocypris bradyi.展开更多
基金Supported by Supported by the United Nations Environment Program(DXAL-2014-002)
文摘To understand the status of phytoplankton community of the Genhe River in the summer of 2015,we investigated the phytoplankton in Genhe River. We identified 5 phyla and 36 species,among which Bacillariophyta(23) were the most,followed by Chlorophyta(10),Cyanophyta(1),Chrysophyta(1),Pyrrophyta(1). The phytoplankton abundance was(15. 6-810) × 104 ind·L^(-1); the biomass was(0. 07-2. 876) mg·L-1; Shannon-wiener index was 1. 05-3. 24; Pielou evenness index was 0. 27-0. 96. Using Shannon-wiener index and Pielou index,the water quality of Genhe River was assessed,and the results showed that the water quality was the best at 5# sampling point,the water quality was good in 3#,4#,7#,8#,9# sampling points,and there was a state of pollution at other sampling points. Canonical correspondence analysis and Pearson correlation analysis showed that iron ion,transparency,p H value,water depth and water temperature were important environmental factors that affect the distribution of phytoplankton,and copper ion,nitrite ion and COD also significantly affected the distribution of phytoplankton.
文摘A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.
基金supported by the National Natural Science Foundation of China(No.51279033).
文摘Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.
基金Financial support for the study was provided by the China Postdoctoral Science Foundation (Grant No. 2015M570431)the Jiangsu Provincial Natural Science Foundation of China (Grant No. BK20150579)the State High Technology Funds of China (Grant No. 2009AA122001)
文摘Ecological geographic regions, also called eco-regions, can be used to divide a remotely sensed image, which is helpful for reducing the complexity of land cover types within eco-regions and for improving the classification accuracy of land cover. In this case study in China, we improved a method of ecological geographic regionalization that is more suitable for remote sensing mapping of regional land cover, and we obtained new eco-regions. The canonical correspondence analysis(CCA) and k-means clustering were adopted in the ecological geographic regionalization using both seasonal remotely-sensed vegetation information and environmental data including climate, elevation and soil features. Our results show that the combination of seasonal vegetation information and the CCA performed well in the selection of the dominant environmental factor of the biogeographic pattern, and it can be used as regionalization indicators of eco-regions. We found that thermal factors are the most important driving forces of the biogeographic pattern in China, which followed by moisture factors. Two global land cover products(MODIS MCD12C1 and Glob Cover) were used to assess our eco-regions. The results show that our eco-regions performed better than that of a previous study regarding the complexity of land cover types, such as in the number of types and the proportional area of the major/secondary type. These results indicate that the method of ecological geographic regionalization, which is based on environmental factors associated with seasonal vegetation features, is effective for reducing the regional complexity of land cover.
基金supported by the National Natural Science Foundation of China (41372179)the Special Funds for Public Welfare Land and Resources Scientific Project (201311140)China Geological Survey (12120114048501)
文摘103 surface sediment samples in 71 water bodies, such as lakes with different salinity, swamps, shallow puddles and rivers on the Qinghai-Tibetan Plateau （QTP）, were collected to study the ecological distribution of living ostracods and their environmental implications. Total of 12 genus and 45 species living ostracods are identified. According to the frequencies and abundance of ostracods occurrence, Limnocythere dubiosa, Limnocytherellina kunlunensis, llyocypris bradyi, Candona candida, Eucypris rischtanica and Leucocythere dilatata are the common species on the QTP, with occurrence frequency of more than 8 and abundance of more than 570 in the 71 water bodies. Among them, L. dubiosa, occurring in 28 water bodies with 2177 shells, is the most widely distributed ostracod in this research. Canonical Correspondence Analysis （CCA） indicates high correlation between species and environmental variables, suggesting that the occurrence of species is strongly related to the changes in ecological conditions of habitats. Among eight environmental factors, salinity and pH value are the most affective variables that influence the species occurrence. L. kunlunensis is positively correlated with salinity while E. rischtanica is negatively correlated with salinity. C. candida has a positive correlation with salinity, as does I. bradyi although there is not such a strong correlation. L. dubiosa displays a positive correlation with pH value. Consequently, we discuss the environmental implications of the common living ostracods on the QTP based on the CCA as well as the distribution of ostracod species in different salinity and pH values water. L. dubiosa, L. kunlunensis and E. rischtanica are euryhaline species, among which, L. dubiosa is the most adaptable species on the QTP with large occurrence in sundry salinity water and the most widely adaptive range for pH values. L. kunlunensis prefers to saline water while E. rischtanica prefers to fresh water. Both L. kunlunensis and E. rischtanica can live in water from faintly acid to alkaline, in contrast, L. dubiosa only appears in neutral and alkaline water bodies. L bradyi only occurs in fresh water and oligohaline water with a large pH tolerance range tolerance range from weakly acidic water to alkaline water weakly acidic water to alkaline water. C. Candida lives in freshwater, with pH value above eight. The six common species reach maximum abundance in alkaline water （pH 8-10） except for llyocypris bradyi.
