Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Broadband terahertz time-domain spectroscopy of drugs-of-abuse and the use of principal component analysis

EPSRC, the European Community IST FP6 Integrated, etc

PCA在语音检测中的应用研究

提出主元分析PCA(Principal Component Analysis)用于语音检测的方法研究．用主元分析法在多维空间中建立坐标轴，将待处理信号投影到该坐标轴中，通过分析投影结果判断是否为语音信号．通过将语音和非语音分别建立子空间，来区分语音和非语音信号．该方法不同于常规的语音时域、频域处理方法，而是在多维空间中对信号进行分析·实验结果表明，该方法准确率高、简单、容易实现，而且能区分多种非语音信号．

Spatial distribution of benthic algae in the Gangqu River, Shangrila, China

This study consisted of sampling benthic algae at 32 sites in the Gangqu River, an important upstream tributary of the Yangtze River. Our aims were to characterize the benthic algae communities and relationships with environmental variables. Among the 162 taxa observed, Achnanthes linearis and Achnanthes lanceolata var. elliptica were the dominant species (17.10% and 14.30% of the total relative abundance, respectively). Major gradients and principal patterns of variation within the environmental variables were detected by principal component analysis (PCA). Then non-metric multidimensional scaling (NMS) divided all the sites into three groups, which were validated by multi-response permutation procedures (MRPP). Canonical correspondence analysis (CCA) indicated that three environmental variables (TN, TDS, and TP) significantly affected the distribution of benthic algae. Weighted averaging regression and cross-calibration produced strong models for predicting TN and TDS concentration, which enabled selection of algae taxa as potentially sensitive indicators of certain TN and TDS levels: for TN, Achnanthes lanceolata, Achnanthes lanceolata var. elliptica, and Cymbella ventricosa var. semicircularis; for TDS, Cocconeis placentula, Cymbella alpina var. minuta, and Fragilaria virescens. The present study represents an early step in establishing baseline conditions. Further monitoring is suggested to gain a better understanding of this region.

Effects of seasonal succession and water pollution on the protozoan community structure in an eutrophic lake

The purpose of the research is to study the seasonal succession of protozoa community and the effect of water quality on the protozoa community to characterize biochemical processes occurring at a eutrophic Lake Donghu, a large shallow lake in Wuhan City, China. Samples of protozoa communities were obtained monthly at three stations by PFU (polyurethane foam unit) method over a year. Synchronously, water samples also were taken from the stations for the water chemical quality analysis. Six major variables were examined in a principal component analysis (PCA), which indicate the fast changes of water quality in this station I and less within-year variation and a comparatively stable water quality in stations II and III. The community data were analyzed using multivariate techniques, and we show that clusters are rather mixed and poorly separated, suggesting that the community structure is changing gradually, giving a slight merging of clusters form the summer to the autumn and the autumn to the winter. Canonical correspondence analysis (CCA) was used to infer the relationship between water quality variables and phytoplankton community structure, which changed substantially over the survey period. From the analysis of cluster and CCA, coupled by community pollution value (CPV), it is concluded that the key factors driving the change in protozoa community composition in Lake Donghu was water qualities rather than seasons. (c) 2006 Elsevier Ltd. All rights reserved.

Population genetic structure of Carchesium polypinum (Ciliophora : Peritrichia) in four chinese lakes inferred from ISSR fingerprinting: High diversity but low differentiation

Although the peritrichous ciliate Carchesium polypinum is common in freshwater, its population genetic structure is largely unknown. We used inter-simple sequence repeat (ISSR) fingerprinting to analyze the genetic structure of 48 different isolates of the species from four lakes in Wuhan, central China. Using eight polymorphic primers, 81 discernible DNA fragments were detected, among which 76 (93.83%) were polymorphic, indicating high genetic diversity at the isolate level. Further, Nei's gene diversity (h) and Shannon's Information index (I) between the different isolates both revealed a remarkable genetic diversity, higher than previously indicated by their morphology. At the same time, substantial gene flow was found. So the main factors responsible for the high level of diversity within populations are probably due to conjugation (sexual reproduction) and wide distribution of swarmers. Analysis of molecular variance (AMOVA) showed that there was low genetic differentiation among the four populations probably due to common ancestry and flooding events. The cluster analysis and principal component analysis (PCA) suggested that genotypes isolated from the same lake displayed a higher genetic similarity than those from different lakes. Both analyses separated C. polypinum isolates into subgroups according to the geographical locations. However, there is only a weak positive correlation between the genetic distance and geographical distance, suggesting a minor effect of geographical distance on the distribution of genetic diversity between populations of C. polypinum at the local level. In conclusion, our studies clearly demonstrated that a single morphospecies may harbor high levels of genetic diversity, and that the degree of resolution offered by morphology as a marker for measuring distribution patterns of genetically distinct entities is too low.

Mobility and profiles of polychlorinated dibenzo-p-dioxins and dibenzofurans in sediment of Ya-Er Lake, China

The sediment of Ya-Er Lake had been heavily polluted by polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) from the former chloralkali industry. The total amounts of PCDD/Fs and I-TEQ decreased along the water flow direction and also decreased from top to bottom layers of sediment cores. Sediment of Pond 1 was dominated by PCDF, especially TCDF. In contrast, in the other four ponds, PCDD dominated in all layers and octachlorinated dibenzo-p-dioxin (OCDD) predominated in all of the homologues. When homologue profiles from sediments and water samples were compared using principal component analysis (PCA), the first two principal components represented 95.2% of the variance in the data. The first component explained 75.9% of the variance and the second one 19.3%. Two clusters were most distinct, presenting a shift in PCDD/Fs composition from PCDF to heptachlorinated dibenzo-p-dioxin (HpCDD) and OCDD in sediments and water from Pond I to Ponds 2-5. The pattern variation between Pond 1 and Ponds 2-5 in Ya-Er Lake was most likely due to the change of process in the chemical plant after the dams between the ponds were built. The results of the present study also showed that log K-oc of PCDD/Fs calculated from data of sediment and water in the field were comparable with theoretical log K-oc. The results also implied that the concentrations of PCDD/Fs in water and sediments could be predicted from each other by log K-oc. (C) 2001 Elsevier Science Ltd. All rights reserved.

Fast Discrimination of Commerical Corn Varieties Based on Near Infrared Spectra

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenurnber range from 4 000 to 12 000 cm (1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.

A New Discrimination Method of Maize Seed Varieties Based on Near-Infrared Spectroscopy

A new discrimination method for the maize seed varieties based on the near-infrared spectroscopy was proposed. The reflectance spectra of maize seeds were obtained by a FT-NIR spectrometer (12 000-4 000 cm(-1)). The original spectra data were preprocessed by first derivative method. Then the principal component analysis (PCA) was used to compress the spectra data. The principal components with the cumulate reliabilities more than 80% were used to build the discrimination models. The model was established by Psi-3 neuron based on biomimetic pattern recognition (BPR). Especially, the parameter of the covering index was proposed to assist to discriminating the variety of a seed sample. The authors tested the discrimination capability of the model through four groups of experiments. There were 10, 18, 26 and 34 varieties training the discrimination models in these experiments, respectively. Additionally, another seven maize varieties and nine wheat varieties were used to test the capability of the models to reject the varieties not participating in training the models. Each group of the experiment was repeated three times by selecting different training samples at random. The correct classification rates of the models in the four-group experiments were above 91. 8%. The correct rejection rates for the varieties not participating in training the models all attained above 95%. Furthermore, the performance of the discrimination models did not change obviously when using the different training samples. The results showed that this discrimination method can not only effectively recognize the maize seed varieties, but also reject the varieties not participating in training the model. It may be practical in the discrimination of maize seed varieties.

土丘陵沟壑区植被恢复过程中的土壤抗蚀与细沟侵蚀演变

对黄土丘陵沟壑区安塞纸坊沟和县南沟、延安燕沟3个流域不同恢复年限的植物群落的土壤抗蚀性和侵蚀程度进行了研究。对12个土壤抗蚀性指标进行主成分分析表明,土壤抗蚀性(主成分综合指数)强弱为灌木群落阶段>多年生草本和蒿类群落阶段>一二年生草本群落阶段,与一二年生草本群落阶段相比,灌木群落阶段与多年生草本和蒿类群落阶段的土壤抗蚀性分别增加了362.29%～673.33%和574.71%～930.00%;野外调查结果分析表明,随着植被的恢复演替,土壤侵蚀量呈现明显的下降趋势,灌木群落阶段的土壤侵蚀量仅为演替初期的1.42%～5.59%;通过回归分析,土壤侵蚀量和水稳性团聚类因子,以及有机质含量之间分别存在极显著与显著相关关系,鉴于土壤分析的易获性,可选择>0.5mm水稳性团聚体与有机质含量作为反映土壤侵蚀程度的指标。

A QUANTITATIVE ASSESSMENT OF SOURCE CONTRIBUTIONS TO INHALABLE PARTICULATE MATTER POLLUTION IN METROPOLITAN BOSTON

In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.

NMR and pattern recognition studies on the acute biochemical effects of Lu(NO3)(3)

Pattern recognition methods were applied to the analysis of 600 MHz H-1 NMR spectra of urine from rats dosed with compounds that induced organ-specific damage in the liver and kidney. Male Wistar rats were separated into groups (n=4) and each was treated with one of following compounds: HgCl2, CCl4, Lu(NO3)(3) and Changle (a kind of rare earth complex mixed with La, Ce, Pr and Nd). Urine samples from the rats dosed with HgCl2, CCl4 and Lu(NO3)(3) were collected over a 24 h time course and the samples from the rats administrated with Changle were gained after 3 months. These samples were measured by 600 MHz NMR spectroscopy. Each spectrum was data-processed to provide 223 intensity-related descriptors of spectra. Urine spectral data corresponding to the time intervals, 0-8 h (HgCl2 and CCl4), 4-8 (Lu(NO3)(3)) h and 90 d (Changle) were analyzed using principal component analysis (PCA). Successful classification of the toxicity and biochemical effects of Lu(NO3)(3) was achieved.