A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the rel...A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.展开更多
This paper presents a novel face recognition algorithm. To provide additional variations to training data set, even-odd decomposition is adopted, and only the even components (half-even face images) are used for furth...This paper presents a novel face recognition algorithm. To provide additional variations to training data set, even-odd decomposition is adopted, and only the even components (half-even face images) are used for further processing. To tackle with shift-variant problem,Fourier transform is applied to half-even face images. To reduce the dimension of an image,PCA (Principle Component Analysis) features are extracted from the amplitude spectrum of half-even face images. Finally, nearest neighbor classifier is employed for the task of classification. Experimental results on ORL database show that the proposed method outperforms in terms of accuracy the conventional eigenface method which applies PCA on original images and the eigenface method which uses both the original images and their mirror images as training set.展开更多
In this paper, gas chromatography-mass spectrometry （GS-MS） was used to build the standard fingerprint of volatile oil from Rosa multiflora Thunb. from 12 different habitats. Fourteen components in the volatile oil ...In this paper, gas chromatography-mass spectrometry （GS-MS） was used to build the standard fingerprint of volatile oil from Rosa multiflora Thunb. from 12 different habitats. Fourteen components in the volatile oil were identified as the indicator components ofR. multiflora, of which one was selected as the standard. The GC analysis conditions used for fingerprinting afford a very good separating effect. The similarity of the 12 volatile oils from R. multiflora Thunb. was more than 0.84, and the precision, stability and repeatability of the fingerprints were quite good. It could be concluded that the fingerprints can be used as the standard and as a quality control method for medicinal materials from R. multiflora Thunb..展开更多
文摘采用外周血淋巴细胞培养及G带染色体标本制作技术,研究和分析华南虎(Panthera tigris amoyensis)染色体的核型和带型。结果表明:华南虎二倍体染色体数为2n=38条,其中常染色体18对,性染色体1对。常染色体按相对长度从长到短依次编号为1~18。根据着丝粒指数可将华南虎染色体分为4组,即A组(m),包括2、5、13、18和X;B组(Sm),包括1、4、7、8、9、10、11、12、14、17和Y;C组(St),包括3、6;D组(t),包括15、16。核型公式为8(m)+20(Sm)+4(St)+4(t),XY(m,Sm)/XX(m,m)。本研究成功制备了华南虎染色体核型标本,初步建立了华南虎染色体G带核型模式图谱。经比对,发现华南虎与东北虎(P. t. altaica)染色体核型存在明显差异,可为虎亚种的分类研究提供依据,同时能为华南虎种群基因多样性及遗传学研究提供新的参考和开辟新的途径。
基金Sponsored by the National Natural Science Foundation of China(Grant No. 60272073, 60402025 and 60802059)by Foundation for the Doctoral Program of Higher Education of China (Grant No. 200802171003)
文摘A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel.The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information.Then the sub-pixel scaled target could be predicted by the trained model.In order to improve the performance of BP network,BP learning algorithm with momentum was employed.The experiments were conducted both on synthetic images and on hyperspectral imagery(HSI).The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.
文摘This paper presents a novel face recognition algorithm. To provide additional variations to training data set, even-odd decomposition is adopted, and only the even components (half-even face images) are used for further processing. To tackle with shift-variant problem,Fourier transform is applied to half-even face images. To reduce the dimension of an image,PCA (Principle Component Analysis) features are extracted from the amplitude spectrum of half-even face images. Finally, nearest neighbor classifier is employed for the task of classification. Experimental results on ORL database show that the proposed method outperforms in terms of accuracy the conventional eigenface method which applies PCA on original images and the eigenface method which uses both the original images and their mirror images as training set.
文摘In this paper, gas chromatography-mass spectrometry （GS-MS） was used to build the standard fingerprint of volatile oil from Rosa multiflora Thunb. from 12 different habitats. Fourteen components in the volatile oil were identified as the indicator components ofR. multiflora, of which one was selected as the standard. The GC analysis conditions used for fingerprinting afford a very good separating effect. The similarity of the 12 volatile oils from R. multiflora Thunb. was more than 0.84, and the precision, stability and repeatability of the fingerprints were quite good. It could be concluded that the fingerprints can be used as the standard and as a quality control method for medicinal materials from R. multiflora Thunb..
