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Identification of plant-pathogenic fungi is time-consuming due to cultivation and microscopic examination and can be influenced by the interpretation of the micro-morphological characters observed. The present investigation aimed to create a simple but sophisticated method for the identification of plant-pathogenic fungi by Fourier transform infrared (FTIR) spectroscopy. In this study, FTIR-attenuated total reflectance (ATR) spectroscopy was used in combination with chemometric analysis for identification of important pathogenic fungi of horticultural plants. Mixtures of mycelia and spores from 27 fungal strains belonging to nine different families were collected from liquid PD or solid PDA media cultures and subjected to FTIR-ATR spectroscopy measurements. The FTIR-ATR spectra ranging from 4 000 to 400 cm-1 were obtained. To classify the FTIR-ATR spectra, cluster analysis was compared with canonical vitiate analysis (CVA) in the spectral regions of 3 050～2 800 and 1 800～900 cm-1. Results showed that the identification accuracies achieved 97.53% and 99.18% for the cluster analysis and CVA analysis, respectively, demonstrating the high potential of this technique for fungal strain identification.

Celestial spectrum contains a great deal of astrophysical information. Through the analysis of spectra, people can get the physical information of celestial bodies, as well as their chemical composition and atmospheric parameters. With the implementation of LAMOST, SDSS telescopes and other large-scale surveys, massive spectral data will be produced, especially along with the formal operation of LAMOST, 2 000 to 4 000 spectral data will be generated each observation night. It requires more efficient processing technology to cope with such massive spectra. Automatic classification of stellar spectra is a basic content of spectral processing. The main purpose of this paper is to research the automatic classification of massive stellar spectra. The Lick index is a set of standard indices defined in astronomical spectra to describe the spectral intensity of spectral lines, which represent the physical characteristics of spectra. Lick index is a relatively wide spectral characteristics, each line index is named after the most prominent absorption line. In this paper, the Bayesian method is used to classify stellar spectra based on Lick line index, which divides stellar spectra to three subtypes: F, G, K. First of all, Lick line index of spectra is calculated as the characteristic vector of spectra, and then Bayesian method is used to classify these spectra. For massive spectra, the computation of Lick indices and the spectral classification using Bayesian decision method are implemented on Hadoop. With use of the high throughput and good fault tolerance of HDFS, combined with the advantages of MapReduce parallel programming model, the efficiency of analysis and processing for massive spectral data have been improved significantly. The main innovative contributions of this thesis are as follows. (1) Using Lick indices as the characteristic to classify stellar spectra based on Bayesian decision method. (2) Implementing parallel computation of Lick indices and parallel classification of stellar spectra using Bayesian based on Hadoop MapReduce distributed computing framework.

LAMOST-DR1 is the first data released by Guoshoujing telescop, which has the largest number of stellar spectra in the world at present. The data set provides the data source for searching for special and rare celestial objects like cataclysmic variable stars.Meanwhile, it requires more advanced astronomical data processing methods and techniques. A data mining method for cataclysmic variable spectra in massive spectra data is proposed in this paper. Different types of celestial spectra show obvious difference in the feature space constructed with Laplacian Eigenmap method. The parameters of artificial neural network are optimized with particle swarm optimization method and the total LAMOST-DR1 data is processed. 7 cataclysmic variable star spectra are found in the experiment including 2 dwarf nova, 2 nova like variables and a highly polarized AM Her type. The newly found spectra enrich the current cataclysmic variable spectra library. The experiment is the first attempt of searching for cataclysmic variable star spectra with Guoshoujing telescope data and the results show that our approach is feasible in LAMOST data. This method is also applicable for mining other special celestial objects in sky survey telescope data.

The redshift measurement of galaxy spectrum is a key issue in large astronomical spectral survey. Its goal is to extract the redshift from spectrum, which is caused the Doppler Effect. With the development of the extragalactic sky survey project, the distance (redshift) of the observed targets is becoming further. As a result, the magnitude of the observed objects becomes darker and the spectral quality becomes poorer. Therefore, how to effectively and accurately measure the redshift from these low quality spectra is becoming an important problem in the extragalactic survey. Considering the spectral features and the data character, a new definition of multi-resolution fusion distance for low quality spectra is proposed. In this paper, we put forward a redshift measuring method for low quality galaxy spectra. This method combines the spectral features with different resolutions. The template spectrum and the spectrum to measure are reduced to the resolution and then a distance is computed by combining the offset of the above two spectra in different wavelengths. Then, a fusion distance is weighted averaged from the distances with different resolutions. In this paper, the effect of signal-to-noise ratio (SNR) on the measuring accuracy of the proposed method is discussed. The measuring accuracy is larger than 90% when the SNR is larger than 5. A large number of experiments show that the method proposed in this paper is very efficient in measuring the redshift of the low-quality galaxy spectra and the measuring error has nothing to do with the redshift value. The proposed method can be applied in redshift measurement of galaxies for the large-scale survey data.

Distance metric is an important issue for the spectroscopic survey data processing, which defines a calculation method of the distance between two different spectra. Based on this, the classification, clustering, parameter measurement and outlier data mining of spectral data can be carried out. Therefore, the distance measurement method has some effect on the performance of the classification, clustering, parameter measurement and outlier data mining. With the development of large-scale stellar spectral sky surveys, how to define more efficient distance metric on stellar spectra has become a very important issue in the spectral data processing. Based on this problem and fully considering of the characteristics and data features of the stellar spectra, a new distance measurement method of stellar spectra named Residual Distribution Distance is proposed. While using this method to measure the distance, the two spectra are firstly scaled and then the standard deviation of the residual is used the distance. Different from the traditional distance metric calculation methods of stellar spectra, when used to calculate the distance between stellar spectra, this method normalize the two spectra to the same scale, and then calculate the residual corresponding to the same wavelength, and the standard error of the residual spectrum is used as the distance measure. The distance measurement method can be used for stellar classification, clustering and stellar atmospheric physical parameters measurement and so on. This paper takes stellar subcategory classification as an example to test the distance measure method. The results show that the distance defined by the proposed method is more effective to describe the gap between different types of spectra in the classification than other methods, which can be well applied in other related applications. At the same time, this paper also studies the effect of the signal to noise ratio (SNR) on the performance of the proposed method. The result show that the distance is affected by the SNR. The smaller the signal-to-noise ratio is, the greater impact is on the distance; While SNR is larger than 10, the signal-to-noise ratio has little effect on the performance for the classification.

Vapours of organic matters were determined qualitatively employed with ultraviolet-visible absorption spectroscopy. Vapours of organic matters were detected using ultraviolet-visible spectrophotometer employing polyethylene film as medium, the ultraviolet and visible absorption spectra of vegetable oil vapours of soybean oil, sunflower seed oil, peanut oil, rapeseed oil, sesame oil, cotton seed oil, tung tree seed oil, and organic compound vapours of acetone, ethyl acetate, 95% ethanol, glacial acetic acid were obtained. Experimental results showed that spectra of the vegetable oil vapour and the organic compound vapour could be obtained commendably, since ultra violet and visible spectrum of polyethylene film could be deducted by spectrograph zero setting. Different kinds of vegetable oils could been distinguished commendably in the spectra since the λ(max), λ(min), number of absorption peak, position, inflection point in the ultra violet and visible spectra obtained from the vapours of the vegetable oils were all inconsistent, and the vapours of organic compounds were also determined perfectly. The method had a good reproducibility, the ultraviolet and visible absorption spectra of the vapours of sunflower seed oil in 10 times determination were absolutely the same. The experimental result indicated that polyethylene film as a kind of medium could be used for qualitative analysis of ultraviolet and visible absorption spectroscopy. The method for determination of the vapours of the vegetable oils and organic compounds had the peculiarities of fast speed analysis, well reproducibility, accuracy and reliability and low cost, and so on. Ultraviolet and visible absorption spectrum of organic vapour could provide feature information of material vapour and structural information of organic compound, and provide a novel test method for identifying vapour of compound and organic matter.

We have collected massive stellar spectral data in recent years, which leads to the research on the automatic measurement of stellar atmospheric physical parameters (effective temperature Teff, surface gravity log g and metallic abundance [Fe/ H]) become an important issue. To study the automatic measurement of these three parameters has important significance for some scientific problems, such as the evolution of the universe and so on. But the research of this problem is not very widely, some of the current methods are not able to estimate the values of the stellar atmospheric physical parameters completely and accurately. So in this paper, an automatic method to predict stellar atmospheric parameters based on mass estimation was presented, which can achieve the prediction of stellar effective temperature Teff, surface gravity log g and metallic abundance [Fe/H]. This method has small amount of computation and fast training speed. The main idea of this method is that firstly it need us to build some mass distributions, secondly the original spectral data was mapped into the mass space and then to predict the stellar parameter with the support vector regression (SVR) in the mass space. we choose the stellar spectral data from the United States SDSS-DR8 for the training and testing. We also compared the predicted results of this method with the SSPP and achieve higher accuracy. The predicted results are more stable and the experimental results show that the method is feasible and can predict the stellar atmospheric physical parameters effectively.

This paper presents a method to estimate stellar metallicity based on BP neural network and Ca line index. This method trains a BP ANN model from SDSS/SEGUE stellar spectra and parameters provided by SSPP. The values of Teff and the line index of Ca lines are the input of network while the [Fe/H] values are the oputput of the network. A set of samples are resampled from the set of all and then a network model is trained. The network can be used to predict the stellar metallicity from low-resolution spsectra. The experiment shows that the proposed method can accurately and effectively measure the [Fe/H] from the stellar spectra.

Clubroot, caused by Plasmodiophora brassicae, is considered the most devastating soilborne disease in Brassica crops. It has emerged as a serious disease threatening the cruciferous crop production industry in China. Nowadays, the detection techniques for P. brassicae are laborious, time-consuming and low sensitivity. Rapid and effective detection methods are needed. The objective of this study is to develop a Fourier transform infrared spectrometer (FTIR) technique for detection of P. brassicae effectively and accurately. FTIR and Real-time PCR techniques were applied in quantitative detection of P. brassicae. Chinese cabbages were inoculated with P. brassicae. By analyzing the FTIR spectra of P. brassicae, infected clubroots and healthy roots, three specific bands 1 105, 1 145 and 1 228 cm-1 were selected. According to the correlation between the peak areas at these sensitive bands and Real-time PCR Ct value, quantitative evaluation model of P. brassicae was established based on FTIR y=34. 17 +12. 24x - 9. 81x2 - 6. 05x3, r=0. 98 (p<0. 05). To validate accuracy of the model, 10 clubroot samples were selected randomly from field, and detected by FTIR spectrum model, the results showed that the average error is 1. 60%. This demonstrated that the FTIR technology is an available one for the quantitative detection of P. brassicae in clubroot, and it provides a new method for quantitative and quickly detection of Chinese cabbage clubroot.

The focal ratio degradation (FRD) of optical fiber is one of major sources causing light loss in multi-fiber astronomical instruments. Meanwhile, the sky subtraction is crucial to multi-fiber spectra reduction, especially for the objects which are as faint as the sky background, not to mention for those even fainter ones. To improve the accuracy of sky subtraction, it is necessary to normalize the throughput among object fibers and sky sampling fibers. The rotation and twist during mounting and rotating could change the FRD of individual fibers, which means the variation of the transmission throughput among fibers. We investigate such throughput variation among LAMOST fibers and its correlation with the intensity of sky emission lines on all wavelength coverage in this paper. On the basis of this work, we present an approach to correcting the varied fiber throughput by measuring the intensity of the sky emission lines as the secondary throughput correction. This approach has been applied to LAMOST 2D Pipeline.

In the present paper, a local mean-based K-nearest centroid neighbor (LMKNCN) technique is used for the classification of stars, galaxies and quasars (QSOS). The main idea of LMKNCN is that it depends on the principle of the nearest centroid neighborhood(NCN), and selects K centroid neighbors of each class as training samples and then classifies a query pattern into the class with the distance of the local centroid mean vector to the samples . In this paper, KNN, KNCN and LMKNCN were experimentally compared with these three different kinds of spectra data which are from the United States SDSS-DR8. Among these three methods, the rate of correct classification of the LMKNCN algorithm is higher than the other two algorithms or comparable and the average rate of correct classification is higher than the other two algorithms, especially for the identification of quasars. Experiment shows that the results in this work have important significance for studying galaxies, stars and quasars spectra classification.

There are many valuable rare and unusual objects in spectra dataset of Sloan Digital Sky Survey (SDSS) Data Release eight (DR8), such as special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on, so it is extremely significant to search for rare and unusual celestial objects from massive spectra dataset. A novel algorithm based on Kernel dense estimation and K-nearest neighborhoods (KNN) has been presented, and applied to search for rare and unusual celestial objects from 546 383 stellar spectra of SDSS DR8. Their densities are estimated using Gaussian kernel density estimation, the top 5 000 spectra in descend order by their densities are selected as rare objects, and the top 300 000 spectra in ascend order by their densities are selected as normal objects. Then, KNN were used to classify the rest objects, and simultaneously K nearest neighbors of the 5 000 rare spectra are also selected as rare objects. As a result, there are totally 21 193 spectra selected as initial rare spectra, which include error spectra caused by deletion, redden, bad calibration, spectra consisting of different physically irrelevant components, planetary nebulas, QSOs, special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on. By cross identification with SIMBAD, NED, ADS and major literature, it is found that three DZ white dwarfs, one WDMS, two CVs with company of G-type star, three CVs candidates, six DC white dwarfs, one DC white dwarf candidate and one BL Lacertae (BL lac) candidate are our new findings. We also have found one special DA white dwarf with emission lines of Ca II triple and Mg I, and one unknown object whose spectrum looks like a late M star with emission lines and its image looks like a galaxy or nebula.

Supernova (SN) is called the "standard candles" in the cosmology, the probability of outbreak in the galaxy is very low and is a kind of special, rare astronomical objects. Only in a large number of galaxies, we have a chance to find the supernova. The supernova which is in the midst of explosion will illuminate the entire galaxy, so the spectra of galaxies we obtained have obvious features of supernova. But the number of supernova have been found is very small relative to the large number of astronomical objects. The time computation that search the supernova be the key to weather the follow-up observations, therefore it needs to look for an efficient method. The time complexity of the density-based outlier detecting algorithm (LOF) is not ideal, which effects its application in large datasets. Through the improvement of LOF algorithm, a new algorithm that reduces the searching range of supernova candidates in a flood of spectra of galaxies is introduced and named SKLOF. Firstly, the spectra datasets are pruned and we can get rid of most objects are impossible to be the outliers. Secondly, we use the improved LOF algorithm to calculate the local outlier factors (LOF) of the spectra datasets remained and all LOFs are arranged in descending order. Finally, we can get the smaller searching range of the supernova candidates for the subsequent identification. The experimental results show that the algorithm is very effective, not only improved in accuracy, but also reduce the operation time compared with LOF algorithm with the guarantee of the accuracy of detection.

At present, most sky-subtraction methods focus on the full spectrum, not the particular location, especially for the backgroud sky around [OIII] line which is very important to low redshift quasars. A new method to precisely subtract sky lines in local region is proposed in the present paper, which sloves the problem that the width of Hβ-[OIII] line is effected by the backgroud sky subtraction. The exprimental results show that, for different redshift quasars, the spectral quality has been significantly improved using our method relative to the original batch program by LAMOST. It provides a complementary solution for the small part of LAMOST spectra which are not well handled by LAMOST 2D pipeline. Meanwhile, This method has been used in searching for candidates of double-peaked Active Galactic Nuclei.

The radial velocity of the star is very important for the study of the dynamics structure and chemistry evolution of the Milky Way, is also an useful tool for looking for variable or special objects. In the present work, we focus on calculating the radial velocity of different spectral types of low-resolution stellar spectra by adopting a template matching method, so as to provide effective and reliable reference to the different aspects of scientific research We choose high signal-to-noise ratio (SNR) spectra of different spectral type stellar from the Sloan Digital Sky Survey (SDSS), and add different noise to simulate the stellar spectra with different SNR. Then we obtain theradial velocity measurement accuracy of different spectral type stellar spectra at different SNR by employing a template matching method. Meanwhile, the radial velocity measurement accuracy of white dwarf stars is analyzed as well. We concluded that the accuracy of radial velocity measurements of early-type stars is much higher than late-type ones. For example, the 1-sigma standard error of radial velocity measurements of A-type stars is 5-8 times as large as K-type and M-type stars. We discuss the reason and suggest that the very narrow lines of late-type stars ensure the accuracy of measurement of radial velocities, while the early-type stars with very wide Balmer lines, such as A-type stars, become sensitive to noise and obtain low accuracy of radial velocities. For the spectra of white dwarfs stars, the standard error of radial velocity measurement could be over 50 km x s(-1) because of their extremely wide Balmer lines. The above conclusion will provide a good reference for stellar scientific study.

As the most common stars in the galaxy, M dwarfs can be used to trace the structure and evolution of the Milky Way. Besides, investigating M dwarfs is important for searching for habitability of extrasolar planets orbiting M dwarfs. Spectral classification of M dwarfs is a fundamental work. The authors used DR7 M dwarf sample of SLOAN to extract important features from the range of 600-900 nm by random forest method. Compared to the features used in Hammer Code, the authors added three new indices. Our test showed that the improved Hammer with new indices is more accurate. Our method has been applied to classify M dwarf spectra of LAMOST.

Using the Lick line index, according to the magnanimity characteristics of the spectrum an efficient algorithm of the atmospheric physical parameters measurement by the linear regression method from the point of view of statistical regression was designed. The linear regression was used to achieve the best regression effect by selecting the type of regression and the composition of line index. The formula obtained from the regression model makes the computation speed fast when applied to new data, and the clarity and ease of analysis processing can not be reached by other methods. The experimental results show that through the line index regression method to get the atmospheric physical parameters is feasible.

Template matching is one of the most commonly used methods of automatic stellar spectrum parameter measurement The present paper made comparisons among the three commonly used template matching methods K-Nearest Neighbor (KNN), the Chi-square minimization and cross correlation method. The Continuum normalization and flux normalization were made first. Then the three mentioned methods were compared in measurement results of stellar spectrum parameters. Experiments on SDSS DR8 large sample spectra showed that the cross correlation method had comparable advantages.

In stellar spectral processing, template matching method can be used to obtain comparably ideal results of stellar spectrum parameters by using pattern recognition algorithms, without computing the spectral line-index. The present paper proposed a similarity based method to measure stellar spectrum parameters automatically. First, the continuum normalization was made, and then the similarities between the stellar spectrum and the template spectrum were compared to get more accurate stellar spectrum parameters. Experiments on ELODIE spectrum showed that this method is ideal in efficiently obtaining stellar spectrum parameters.

Automatically determining redshifts of galaxies is very important for astronomical research on large samples, such as large-scale structure of cosmological significance. Galaxies are generally divided into normal galaxies and active galaxies, and the spectra of active galaxies mostly have more obvious emission lines. In the present paper, the authors present a novel method to determine spectral redshifts of active galaxies rapidly based on wavelet transformation mainly, and it does not need to extract line information accurately. This method includes the following steps: Firstly, we denoised a spectrum to be processed; Secondly, the low-frequency spectrum was extracted based on wavelet transform, and then we could get the residual spectrum through the denoised spectrum subtracting the low-frequency spectrum; Thirdly, the authors calculated the standard deviation of the residual spectrum and determined a threshold value T, then retained the wavelength set whose corresponding flux was greater than T; Fourthly, according to the wavelength form of all the standard lines, we calculated all the candidate redshifts; Finally, utilizing the density estimation method based on Parzen window, we determined the redshift point with maximum density, and the average value of its neighborhood would be the final redshift of this spectrum. The experiments on simulated data and real data from SDSS-DR7 show that this method is robust and its correct rate is encouraging. And it can be expected to be applied in the project of LAMOST.

A novel statistic window based method to fit stellar continuum is proposed. First a stellar spectrum is divided into a series of statistic windows in which a certain percent of flux points is selected according to S/N ratio; then low order polynomial iteration fitting is carried out based on the selected flux points to obtain the stellar continuum. Experimental results show that the continuum obtained by the proposed method is more close to the real continuum, compared to other existed methods. This method has a better practical applicability and robustness to all kinds of spectra (except M-type spectrum) in SDSS. It also works well for Guoshoujing Telescope (LAMOST) pilot survey spectra.

In the present paper, we analysed the effects of spectral resolutions and signal-to-noise ratios (SNRs) on 19 atomic absorption line indices of Lick index system. First of all, adopting method of convolving a spectrum with a Gaussian profile, we transformed spectra into those under different resolutions and then measured the line indices on them. Comparisons of the indices under various resolutions allow to investigate the impact of spectral resolution change on the accuracy of measurements of indices. Secondly, by adding random noises with different Gaussian distribution to a spectrum, the authors transformed theoretical spectra with no noises into those under diverse SNRs and then measured line indices on them. Comparisons of the indices under different SNRs greatly helped analyse the influence of SNR on the precision of the measurements of line indices. It comes from comparisons and analysis that the spectral resolution change can cause an index measurement change depending on the extent of the change of spectral resolution. Such a kind of change relationship varies with the indices. The lower the SNR, the less precise the measurements of indices. The effect of SNR on the measurements of indices can be ignored if SNR is larger than 25.

Stellar spectra are characterized by obvious absorption lines or absorption bands, while those with emission lines are usually special stars such as cataclysmic variable stars (CVs), HerbigAe/Be etc. The further study of this kind of spectra is meaningful. The present paper proposed a new method to identify emission line stars (ELS) spectra automatically. After the continuum normalization is done for the original spectral flux, line detection is made by comparing the normalized flux with the mean and standard deviation of the flux in its neighbor region The results of the experiment on massive spectra from SDSS DR8 indicate that the method can identify ELS spectra completely and accurately. Since no complex transformation and computation are involved in this method, the identifying process is fast and it is ideal for the ELS detection in large sky survey projects like LAMOST and SDSS.

An automatic and efficient method for cataclysmic variables candidates is presented in the present paper. The identified CVs were selected as templates. A model was constructed by random forest algorithm with templates and random selected spectra. Wavelength ranking was described by the model and the classifier was constructed afterwards. Most of the non-candidates were excluded by the method. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. 16 new CVs candidates were found in the experiment that shows that our approach to finding special celestial bodies can be feasible in LAMOST.

To restore the continuum and the spectral lines from a noisy astronomical spectrum, then to measure the equivalent widths of the spectral lines, the fractal denoising method was firstly used in astronomical spectra in the present paper. The method is based on the distinguishing features, that is the local self-similarities exist in an astronomical spectrum, while not in a random white noise signal. The experimental results show that the fractal denoising method is efficient in parameter measurements, such as equivalent widths for spectral lines, redshift of galaxies, and so on. In addition, the method can achieve data compression. The fractal method can be used in the mass spectra of LAMOST.

In the present paper the author offers a method to search the QSO candidates and calculate their redshfit using their broad emission lines which are the most important character of quasars. It is hard to identify the lines in the quasar's spectra due to their redshifts distributing on a broad range. Spectra contain two components. One is continuum and the other is lines. The author uses a method of LFPS (low frequency points set) to build the continuum and detect the obvious emission lines, a method that can avoid the broad emission lines as a part of the continuum. The redshift can be calculated by comparing the extracted lines with the line table. The classification can be done with both emission lines and the redshift. For a better accurate rate to recognize the lines, this paper provides a method to estimate the level of the local noise. The method this paper used is independent of the flux calibration of the spectra. It can work for the spectra of the present LAMOST.

Fourier transform infrared (FTIR) attenuated total reflectance (ATR) spectroscopy was used in combination with multivariate statistic analysis for identification of soil-borne fungi that causes severe economic damage to agriculture: Fusarium monili forme, Fusarium semitectum, Fusarium oxysporum, Fusarium solani, Rhizoctonia solani, Sclerotinia sclerotiorum, Pythium aphanidermatum and Phytophthora capsici. The original FTIR spectra were normalized, and the second derivatives were calculated, from which the peak wave numbers showing greatest variability were selected: 2924, 2854, 1745, 1641, 1547, 1466, 1406, 1376, 1306, 1240, 1201, 1152, 1109 and 1028 cm(-1). To discriminate different fungal strains, canonical discriminant analysis and cluster analysis were performed at these characteristic wave numbers. Results showed that the classification accuracies achieved 100% for different species of fungi, and classification accuracies for different fusarium strains achieved 95.56%, demonstrating the high potential of this technique for fungi identification.

An automatic and efficient method for LAMOST's massive spectral data reduction is presented in this paper. The identified cataclysmic variables were selected as templates to construct the feature space by PCA (the principal component analysis), and most of the non-candidates were excluded by the method using support vector machine. Template matching strategy was used to identify the final candidates which were analyzed to complement the templates as feedback. Fifty eight new CVs candidates were found in the experiment, showing that our approach to finding special celestial bodies can be practical in LAM-OST.

Fourier transform infrared spectroscopy (FTIR) technique was applied in the early detection of corynespora spot on cucumber leaves while the symptom had not appeared. The cucumber leaves were inoculated with Corynespora cassiicola. By observing the changes in the FTIR spectra of infected cucumber leaves at various times of post-infection, three sensitive bands, 1 735, 1 545 and 1 240 cm(-1) were selected for the identification of cucumber corynespora leaf spot. According to the peak areas at these sensitive bands, cucumber leaf samples infected with C. cassiicola and control uninfected leaf samples could be classified correctly. Results clearly demonstrated that the FTIR technology is an available one for the early detection of corynespora spot on cucumber leaves while the symptom has not appeared and it provides a new method for the early detection of corynespora spot.

Fourier transform attenuated total reflection infrared spectroscopy (FTIR-ATR) has been a novel technical procedure to identify and classify microorganisms in recent years. In the present study, Fourier transform infrared spectroscopy(FTIR) in combination with an attenuated total reflection (ATR) unit were used to discriminate important plant-destroying fungi. Mycelia of 17 fungal strains belonging to 14 different species were grown on potato dextrose agar (PDA) plants and subjected to FTIR-ATR measurements. High-resolution and well-reproducibility infrared spectra were obtained, and significant spectral differences among these strains were observed in the wavenumber regions of 1800-1485 cm(-1), 1485-1185 cm(-1), and 1185-900 cm(-1). According to the characteristic bands in these regions, cluster analysis was executed to classify the FTIR spectra. The result showed that different fungal strains could be identified correctly, demonstrating the high potential of FTIR-ATR as a tool for fungal strain identification and classification. The method is rapid, inexpensive and reproducible, and requires minimum sample preparation.

The authors present a new method called two class PCA for decomposing the mixed spectra, namely, for subtracting the host galaxy contamination from each SN spectrum. The authors improved the quality of reconstructed galaxy spectrum and computational efficiency, and these improvements were realized because we used both the PCA eigen spectra of galaxy templates library and SN templates library to model the mixed spectrum. The method includes mainly three steps described as follows. The first step is calculating two class PCA eigen spectra of galaxy templates and SN templates respectively. The second step is determining all reconstructed coefficients by the SVD matrix decomposition or orthogonal transformation. And the third step is computing a reconstructed galaxy spectrum and subtracting it from each mixed spectrum. Experiments show that this method can obtain an accurate decomposition of a mixed synthetic spectrum, and is a method with low time-consumption to get the reliable SN spectrum without galaxy contamination and can be used for spectral analysis of large amount of spectra. The time consumption using our method is much lower than that using chi2-template fitting for a spectrum.

Hyperspectral imaging (400-720 nm) and discriminate analysis were investigated for the detection of normal and diseased cucumber leaf samples with powdery mildew (Sphaerotheca fuliginea), angular leaf spot (Pseudomopnas syringae), downy mildew (Pseudoperonospora cubensis), and brown spot (Corynespora cassiicola). A hyperspectral imaging system was es tablished to acquire and pre-process leaf images, as well as to extract leaf spectral properties. Owing to the complexity of the original spectral data, stepwise discriminate and canonical discriminate were executed to reduce the numerous spectral information, in order to decrease the amount of calculation and improve the accuracy. By the stepwise discriminate we selected 12 optimal wavelengths from the original 55 wavelengths, and after the canonical discriminate, the 55 wavelengths were reduced to 2 canonical variables. Then the discriminate models were developed to classify the leaf samples. The result shows that the stepwise discriminate model achieved classification accuracies of 100% and 94% for the training and testing sets, respectively. For the canonical model, the classification accuracies for the training and testing sets were both 100%. These results indicated that it is feasible to identify and classify cucumber diseases using hyperspectral imaging technology and discriminate analysis. The preliminary study, which was done in a closed room with restrictions to avoid interference of the field environment, showed that there is a potential to establish an online field application in cucumber disease detection based on visible spectroscopy.

Supernova (SN) is one of the most intense astronomical phenomena among the known stellar activities, but compared with several billion astronomical objects which people have probed, the number of supernova the authors have observed is very small. Therefore, the authors need to find faster and higher-efficiency approaches to searching supernova. In the present paper, we present a novel automated method, which can be successfully used to reduce the range of searching for 1a supernova candidates in a huge number of galaxy spectra. The theoretical basis of the method is clustering and outlier picking, by introducing and measuring local outlier factors of data samples, description of statistic characters of SN emerges in low dimension space. Firstly, eigenvectors of Peter's 1a supernova templates are acquired through PCA projection, and the description of la supernova's statistic characters is calculated. Secondly, in all data set, the local outlier factor (LOF) of each galaxy is calculated including those SN and their host galaxy spectra, and all LOFs are arranged in descending order. Finally, spectra with the largest first one percent of all LOFs should be the reduced 1a SN candidates. Experiments show that this method is a robust and correct range reducing method, which can get rid of the galaxy spectra without supernova component automatically in a flood of galaxy spectra. It is a highly efficient approach to getting the reliable candidates in a spectroscopy survey for follow-up photometric observation.

A new method for auto-extraction of spectral lines based on sparse representation is presented in the present paper. Firstly, the authors proposed a wavelet denoising scheme using a new theory called sparse representation for noise removal. After performing wavelet transform on the spectral signal, this method implements noise removal by solving an optimization problem, which makes the wavelet coefficients at each scale sparsest. The proposed method not only takes the structure properties in the wavelet coefficients into consideration, but also can well maintain the local characteristics of wavelet coefficients. Therefore it can effectively keep the information of featured spectral lines during the process of denoising. Secondly, the authors got satisfying continua by respectively utilizing the wavelet transform method and spline fitting method. The strong spectral lines were firstly removed from the given spectrum with wavelet transform, leading to the result that the obtained continuum approximated the real one very well. Finally, the spectrum was divided point to point by the obtainable continuum and the normalized spectrum was obtained. And then spectral lines were extracted from the normalized spectrum by using adaptive local thresholding scheme. Experimental results show that the proposed method is effective and efficient in the application of auto-extraction of spectral lines. The authors' method will be also helpful for the automatic classification of astronomical spectra.

The three fundamental parameters of stellar atmosphere, i.e. the effective temperature, the surface gravity, and the metallic, determine the continuum and spectral lines in the stellar spectrum. With the development of the modern telescopes such as SDSS, LAMOST projects, the great voluminous spectra demand to explore automatic celestial spectral analysis methods. It is most significant for Galaxy research to develop automatic methods determining the fundamental parameters from stellar spectra data. Two non-linear regression algorithms, kernel least squared regression (KLSR) and kernel PCA regression (KPCR), are proposed for estimating the three parameters in the present paper. The linear regression models, LSR and PCR, are extended to non-linear regression by using a kernel function for the stellar parameter estimation from spectra. Extensive experiments on low resolution spectra data show: (1) KLSR and KPCR methods realize the regression from spectrum to the effective temperature and gravity. KLSR is sensitive to the noise while KPCR is robust than the former. (2) For the effective temperature estimation, the two algorithms perform similarly; and for the gravity and metallic estimation, the KPCR is superior to the KLSR and the NPR (Non-parameter regression); (3) KLSR and KPCR methods are simple and efficient for the stellar spectral parameter estimation.

Given a set of low-redshift spectra of active galactic nuclei, the wave bands of spectra in the rest frame were intercepted according to the different features of emission lines of broad-line AGNs and narrow-line AGNs, and an adaptive boosting (Adaboost) method was developed to carry out the classification experiments of feature fusion. As a result, the wave band of Halpha and [N II] was confirmed to be the main discriminative feature between broad-line AGNs and narrow-line AGNs. Then based on the wave band of Halpha and [N II], the Adaboost method was used for the spectral classification. In this method, the "weak classifiers" were increased constantly during training until a scheduled error rate or a maximum cycle times was met, then the classification judgment of the consequent collective classifier was determined by the votes of respective judgments of these "weak classifiers". The Adaboost method needs not to adjust parameters in advance and the results of "weak classifiers" are only required to be better than random guessing, so its algorithm is very simple. As proved by the experiments, the adaboost method achieves good performance in the classification just based on the wave band of Halpha and [N II] so that it could be applied effectively to the automatic classification of large amount of AGN spectra from the large-scale spetral surveys.

A combined method of wavelet transform and spline fitting is presented for continuum fitting with the strong spectral lines taken out beforehand. Some comparisons between normal methods and the method presented are made by the experiments on actual spectra And the experimental results show that our method is superior to others. Besides, an effective continuum revision formula is derived to avoid the appearance of negative continua obtained by some methods such as polynomial fitting and so on.

The important astrophysical information is hidden in spectral lines of astronomical spectra. The presen paper presents a method for auto-extraction of spectral lines based on convolution type of wavelet packet. This method consists of four main steps: First, the observed spectra are transformed by convolution type of wavelet packet with 4th scale. Then, the noise with coefficients of the 4th scale is eliminated by the local correlation algorithm and threshold in the wavelet packet domain. After that, middle and high frequency coefficients are selected to reconstruct the feature of the spectral lines. Finally, with the reconstructed feature of the spectral lines, spectral lines in observed spectra are searched. The results of our experiments, which include the spectral lines of stars, normal galaxies and active galaxies, show that the method can robustly and accurately extract the spectral lines. The method was applied to extract the SDSS spectral lines and compute the redshifts with those lines. By comparing the redshifts with those given by SDSS, the extraction has proven successful and practical.

Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space. At first, the K-means clustering is used for the initialization, then based on the sum of squares errors and a gradient descent optimization technique, the number of neurons in the hidden layer is adaptively increased to improve the recognition performance; (3) The quasar spectra recognition is effectively carried out by the above trained RBFN. The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm, but also increase the stability and accuracy of recognition of quasars. Besides, the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project, such as our LAMOST, due to its efficiency.

It is difficult to determine the redshifts of normal galaxies (NG) from their spectra because of their common weak absorption property. In the present work, a novel method is proposed to effectively deal with this issue. The proposed method is composed of the following three parts: At first, the wavelet transform coefficients at the fourth scaling are experimentally found to be appropriate and used as our features to represent the absorption information from NG absorption lines, break points, and absorption bands. Then, the features are mapped by a non-linear method, LLE (locally linear embedding), onto an one-dimensional manifold in the 3D space; Finally, the NG redshifts are obtained by the nearest neighborhood technique from the redshift distribution on the manifold. Besides, the proposed method is compared with widely used PCA method in the literature with SDSS database, and is shown to be more accurate for the redshifts determination.

The effective temperature of a star is one of the most important parameters, which determine the continuum and spectral lines in the stellar spectrum. A non-parameter estimation algorithm is proposed to estimate the stellar effective temperature in the present paper. Firstly, the spectrum data is processed by principal component analysis(PCA), then, an estimating model based on a Gaussian kernel function is set up using the PCA data and their temperatures. Experiments were carried out to verify the efficiency, and numerical robustness of the algorithm is also tested.

Classification and discovery of new types of celestial bodies from voluminous celestial spectra are two important issues in astronomy, and these two issues are treated separately in the literature to our knowledge. In the present paper, a novel coherence measure is introduced which can effectively measure the coherence of a new spectrum of unknown type with the training sampleslocated within its neighbourhood, then a novel classifier is designed based on this coherence measure. The proposed classifier is capable of carrying out spectral classification and knowledge discovery simultaneously. In particular, it can effectively deal with the situation where different types of training spectra exist within the neighbourhood of a new spectrum, and the traditional k-nearest neighbour method usually fails to reach a correct classification. The satisfactory performance for classification and knowledge discovery has been obtained by the proposed novel classifier over active galactic nucleus (AGNs) and active galaxies (AGs) data.

The present paper proposes a model matching method based on density estimation for redshift determination, in whichthe problem of redshift determination is translated into the problem of searching for the point of maximum density within a data set. At first, the mean shift-based method for auto-extraction of spectral lines is used to get feature spectrallines. Secondly, according tothe redshift formula, the authors use the feature wavelength array and the spectral template to get a data set. Finally, the authors findthe point of maximum density within the data set, then the average of the data in epsilon-neighbor of the point is regarded as the redshift estimation. The information of feature wavelength and spectral line type is used in this method so that it can deal with every kind of spectra. Experiments show that our method is stable and the correct identification rate is high.

The mean shift algorithm is used. At first, the property that mean shift vectors always point toward local maxima of the density is used to get the pseudo continuum; secondly, mean shift filtering is a goodedge preserving smoothing, which canadaptively reduce the amount of smoothing near feature spectral lines, so the authors use mean shift filtering in noise reduction after the noramalization of continuum spectra; finally, the authors extract feature spectral lines by setting local thresholds. The experiments on both stars and normal galaxies show that our method can extract spectral lines accurately, which is helpful to the parameter measure and the automatic classification of spectra based on spectral lines.

This paper presents a novel method for redshift determination of quasars. Firstly, a group of redshifts were determined using the emission line info extracted from the observed spectrum; Secondly, the template was redshifted according to the candidates, and the correlation between the observed spectrum and the redshifted template was measured. Finally, the redshift candidate corresponding to the highest correlation was chosen as the redshift. Compared with the existing methods based on spectral line matching, the proposed method has a lower dependence on the quality of spectral line extraction. Experiments show that this method is robust and superior to the methods based on spectral linematching.

Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0.0-0.3, the other of 0.3-0.5, then the PCA is used to extract the main components, and train samples are projected to the main component subspace to obtain characteristic spectra. (2) The characteristic spectra are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoked to locate the redshift value within the estimated range. Compared with the traditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the estimation accuracy. As a result, the proposed method is particularly useful for automatic spectrum processing produced from a large-scale sky survey project.

To measure redshifts of the marge amount of galaxies' spectra automatically is the main goal of the data processing for the LAMOST project (Large Sky Area Multi-Object Optical Fiber Spectroscopic Telescope). A method called PCAZ can be applied to measure the very small redshifts (generally z < 0.2) due to the restriction of the wavelength range of the templates that are composed to make orthogonal templates. In the present article, the authors break the restriction by improving PCAZ method according to the characteristic of LAMOST spectra. Applying this new method to the SDSS data, more than 90% of the results are correct. The maximum limitation for redshift measurement of this new method depends on the wavelength range of the templates and the S/N of the blue parts of the spectra. According to the spectral feature of LAMOST, the authors can measure the galaxies with z < 0.8 correctly. From the experiment the authors concluded: first, this method can be used to measure the redshift of LAMOST spectra; second, the authors need to compose self-contained templates of various galaxies (UV-IR) to measure the survey redshift; finally, the S/N of the blue end of the spectra influences the measurement of the large redshift.

Molybdenum(V) thiocyanate complex extracted by Tween-80, a non-ionic surface active agent, in chloroform has been studied. When the concentration of Tween-80 is 0.1%, extractive ratio can reach 94%, while tungsten(III) thiocyanate can not extracted in the same condition. Therefore, the two kinds of complexes can be separated very well. It enjoys a better extraction efficiency compared with petroleum ether or dimethyl sulfoxide and the advantage of low cost, no toxicity and no pollution. The extraction mechanism has been studied and may be a kind of extraction belonging to the category of supermolecular, which has not been reported publicly.